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Original article
Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records
Ignat Drozdova, Benjamin Szuberta, Ian A. Roweb,c, Timothy J. Kendalld,e, Jonathan A. Fallowfielde,
Corresponding author
Jonathan.Fallowfield@ed.ac.uk

Corresponding author.
a Bering Limited, London, UK
b Leeds Institute of Medical Research, University of Leeds, UK
c Leeds Liver Unit, St James's University Hospital, Leeds Teaching Hospitals, UK
d Edinburgh Pathology, University of Edinburgh, Edinburgh, UK
e Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="cesectitle0008">Introduction</span><p id="para0005" class="elsevierStylePara elsevierViewall">Metabolic dysfunction-associated steatotic liver disease &#40;MASLD&#41;&#44; previously termed nonalcoholic fatty liver disease &#40;NAFLD&#41; &#91;<a class="elsevierStyleCrossRef" href="#bib0001">1</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0002">2</a>&#93;&#44; is defined as the presence of hepatic steatosis in conjunction with at least one cardiometabolic risk factor &#40;obesity&#44; hypertension&#44; type 2 diabetes&#44; dyslipidemia&#41; but no discernible secondary causes&#44; including substantial alcohol intake&#44; medications known to cause steatosis&#44; or inherited metabolic conditions &#91;<a class="elsevierStyleCrossRef" href="#bib0003">3</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0004">4</a>&#93;&#46; Around 25 &#37; of people with hepatic steatosis progress to metabolic dysfunction-associated steatohepatitis &#40;MASH&#41;&#44; which is characterized by hepatocellular ballooning and lobular necroinflammation&#44; and an increased risk of fibrosis&#44; cirrhosis&#44; hepatic decompensation&#44; hepatocellular carcinoma &#40;HCC&#41;&#44; and all-cause mortality &#91;<a class="elsevierStyleCrossRef" href="#bib0003">3</a>&#93;&#46; The global burden of MASLD is increasing at an alarming rate &#91;<a class="elsevierStyleCrossRef" href="#bib0005">5</a>&#93;&#44; with a worldwide prevalence of up to 32&#46;4 &#37; &#91;<a class="elsevierStyleCrossRef" href="#bib0006">6</a>&#93;&#46; The health economic impacts of MASLD are considerable&#44; including annual direct medical costs of &#36;103 billion &#40;&#36;1613 per patient&#41; in the United States &#40;US&#41; and &#8364;35 billion &#40;&#8364;1163 per patient&#41; in the Europe-4 countries &#40;Germany&#44; France&#44; Italy&#44; and the United Kingdom&#41; &#91;<a class="elsevierStyleCrossRef" href="#bib0007">7</a>&#93;&#46;</p><p id="para0006" class="elsevierStylePara elsevierViewall">Although the past four decades have witnessed major advances in the biological understanding of MASLD and the mechanisms driving the pathogenesis of cirrhosis and HCC&#44; there has been little translation to improved clinical outcomes &#91;<a class="elsevierStyleCrossRef" href="#bib0008">8</a>&#93;&#46; This can be explained&#44; in part&#44; by the lack of approved therapy &#91;<a class="elsevierStyleCrossRef" href="#bib0009">9</a>&#93; and the heterogeneous natural history of the MASLD&#44; which includes extra-hepatic manifestations such as cardiovascular disease &#40;CVD&#41; and chronic kidney disease &#40;CKD&#41; that can further increase disease burden and contribute to the risk of all-cause mortality &#91;<a class="elsevierStyleCrossRef" href="#bib0010">10</a>&#93;&#46; Indeed&#44; in many series&#44; the leading cause of death in individuals with MASLD is CVD&#44; followed by extra-hepatic cancers&#44; and then liver-related mortality &#91;<a class="elsevierStyleCrossRef" href="#bib0011">11</a>&#93;&#44; highlighting the importance of a holistic approach to the management of this patient population&#46; Moreover&#44; there is an urgent need to embed effective strategies within primary care&#44; as well as hepatology services&#44; to enable early risk stratification and evidence-based treatment initiation to curtail MASLD-associated morbidity and mortality&#46;</p><p id="para0007" class="elsevierStylePara elsevierViewall">The increased availability of electronic health records &#40;EHRs&#41; opens new opportunities to develop predictive case-finding algorithms that facilitate effective MASLD surveillance &#91;<a class="elsevierStyleCrossRef" href="#bib0012">12</a>&#93;&#46; EHR adoption has reached near-universal levels in the US and the European Union in both acute care hospitals and primary care &#91;<a class="elsevierStyleCrossRef" href="#bib0013">13</a>&#93;&#46; Moreover&#44; studies have shown the potential utility of applying artificial intelligence &#40;AI&#41; and machine learning &#40;ML&#41; algorithms to EHR data to improve the early detection&#44; diagnosis&#44; and management of many conditions&#44; particularly cardiometabolic diseases &#91;<a class="elsevierStyleCrossRef" href="#bib0014">14</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0015">15</a>&#93;&#46; However&#44; despite the proliferation of machine-readable datasets&#44; the development and scaling of predictive models have been limited&#46; The complexities of real-world clinical data&#44; replete with thousands of potential predictor variables and missing values&#44; are seen as the key barriers to implementation &#91;<a class="elsevierStyleCrossRefs" href="#bib0016">16-18</a>&#93;&#46; Deep neural networks &#40;DNNs&#41; have emerged as robust tools with applications to sequence prediction within mixed modality data sets &#91;<a class="elsevierStyleCrossRefs" href="#bib0017">17-20</a>&#93;&#46; The key advantages of DNN methods are their ability to handle large volumes of relatively noisy data&#44; including errors in labels&#44; as well as large numbers of input variables &#91;<a class="elsevierStyleCrossRef" href="#bib0018">18</a>&#93;&#46;</p><p id="para0008" class="elsevierStylePara elsevierViewall">Because liver-related outcomes in MASLD are strongly associated with the severity of liver fibrosis &#91;<a class="elsevierStyleCrossRef" href="#bib0021">21</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0022">22</a>&#93;&#44; existing risk stratification is anchored to the histological stage or non-invasive assessment of fibrosis using surrogate markers &#91;<a class="elsevierStyleCrossRefs" href="#bib0023">23-25</a>&#93;&#44; but such approaches may not reflect the complexity and multimorbidity of MASLD&#46; Furthermore&#44; despite the increasing application of AI and ML tools to MASLD disease management&#44; predictive analysis has largely focused on diagnosis and screening &#91;<a class="elsevierStyleCrossRef" href="#bib0026">26</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0027">27</a>&#93;&#44; as well as disease quantification &#91;<a class="elsevierStyleCrossRef" href="#bib0028">28</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0029">29</a>&#93;&#46; Therefore&#44; there is a critical need for a reliable and accessible risk stratification approach for broad clinical outcomes of interest&#44; such as all-cause mortality&#44; to enable early&#47;proactive community interventions such as lifestyle adjustments and future care planning&#46;</p><p id="para0009" class="elsevierStylePara elsevierViewall">In this work&#44; we test the hypothesis that a simple Transformer neural network&#44; trained on routinely collected in-patient and out-patient data from people with MASLD&#44; can be used effectively to predict individuals at an increased risk of all-cause mortality&#46;</p></span><span id="sec0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="cesectitle0009">Patients and Methods</span><span id="sec0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;1</span><span class="elsevierStyleSectionTitle" id="cesectitle0010">Study populations</span><p id="para0010" class="elsevierStylePara elsevierViewall">The SteatoSITE retrospective dataset was drawn from a population representing 12 of the 14 territorial Health Boards in Scotland and consists of <span class="elsevierStyleItalic">n</span> &#61; 940 histologically defined patients &#40;55&#46;4 &#37; men and 44&#46;6 &#37; women&#59; median body mass index 31&#46;3&#59; 32 &#37; with type 2 diabetes&#41; covering the complete MASLD severity spectrum&#46; Detailed characteristics of the SteatoSITE cohort have been published &#91;<a class="elsevierStyleCrossRef" href="#bib0030">30</a>&#93;&#46; For the validation study&#44; a nested case-control design was used on an independent non-biopsy MASLD patient population &#40;<span class="elsevierStyleItalic">n</span> &#61; 528 patients&#41; from NHS Greater Glasgow and Clyde &#40;GG&#38;C&#41; collected between 2002 and 2021&#46;</p><p id="para0011" class="elsevierStylePara elsevierViewall">For SteatoSITE&#44; cases with a liver tissue sample acquired between January 2000 and October 2019 and a histological diagnosis of NAFLD &#40;MASLD&#41; were included&#46; The other inclusion&#47;exclusion criteria were&#58; men or women&#59; &#62;18 years of age at the time of tissue sampling&#59; all ethnic groups&#44; socio-economic backgrounds&#44; and health status&#59; dead or alive at the time of inclusion into data commons&#59; no documented history of chronic liver disease of any non-MASLD etiology&#44; including alcohol-related liver disease&#44; chronic viral hepatitis&#44; hemochromatosis&#44; Wilson disease&#44; autoimmune hepatitis&#44; primary biliary cholangitis&#44; primary sclerosing cholangitis&#59; and patients with excessive alcohol use documented within the clinical data supplied on the specimen request form &#40;&#62;21 units&#47;week for men&#44; &#62;14 units&#47;week for women&#41;&#59; or histological features suggesting a secondary non-MASLD diagnosis&#46;</p><p id="para0012" class="elsevierStylePara elsevierViewall">The SteatoSITE dataset was used for model training and in-sample validation using five-fold cross-validation&#46; During each cross-validation run&#44; the dataset was partitioned into training&#44; validation&#44; and testing subsets such that the distribution of age&#44; gender&#44; and outcomes were stratified across each partition&#46; To avoid data leakage across data partitions&#44; we ensured that there were no overlapping patient identifiers&#46;</p><p id="para0013" class="elsevierStylePara elsevierViewall">The NHS GG&#38;C dataset comprised of patient EHRs obtained between 2000 and 2019 and followed similar inclusion and exclusion criteria to SteatoSITE&#44; although the clinical diagnosis was based on the International Classification of Diseases &#8211; Tenth Revision &#40;ICD-10&#41; codes &#40;K76&#46;0 &#91;NAFLD&#44; all&#93; and K75&#46;8 NASH&#41;&#46; The NHS GG&#38;C dataset was used for out-of-sample model validation&#46;</p><p id="para0014" class="elsevierStylePara elsevierViewall">Notably&#44; ICD diagnostic coding for inpatient and outpatient episodes and procedures &#40;OPCS Classification of Interventions and Procedures &#40;OPCS-4&#41;&#41; for both study populations followed recent expert consensus guidelines for using administrative coding in EHR-based research of MASLD &#91;<a class="elsevierStyleCrossRef" href="#bib0031">31</a>&#93;&#46;</p><p id="para0015" class="elsevierStylePara elsevierViewall">We categorized the cause of death based on the methods used by Simon <span class="elsevierStyleItalic">et al</span>&#46; &#91;<a class="elsevierStyleCrossRef" href="#bib0032">32</a>&#93;&#46; The following ICD-10 and OPCS-4 codes were used for the cause-specific categories&#58; HCC &#40;&#39;C22&#8242;&#44; &#39;C220&#8242;&#44; &#39;C229&#8242;&#44; &#39;C2299&#8242;&#41;&#59; cirrhosis &#40;Y830&#44; T864&#44; K74&#44; K72&#44; K767&#44; I8&#42; &#40;includes other decompensation causes and post-transplant complications&#41;&#41;&#59; non-HCC cancer &#40;any C code apart from those for HCC&#41;&#59; cardiovascular disease &#40;any I code apart from I8&#42; &#40;varices&#41;&#41;&#59; other &#40;none of the above&#41;&#46; Additionally&#44; we filtered sequentially down the hierarchy of cause of death information and used the first non-other code that appeared&#46;</p></span><span id="sec0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;2</span><span class="elsevierStyleSectionTitle" id="cesectitle0011">Data representation and ground truthing</span><p id="para0016" class="elsevierStylePara elsevierViewall">Each patient&#39;s longitudinal EHR vector was split into an Observation and Prediction Window &#40;<a class="elsevierStyleCrossRef" href="#fig0001">Fig&#46; 1</a>&#41;&#46; The Index Date for case patients was calculated as the date 12&#44; 24&#44; or 36 months before the patient&#39;s date of death&#46; The Index Date for controls was calculated as the date 12&#44; 24&#44; or 36 months before the last EHR entry&#46; The Observation Window comprised all EHR vectors during a ten-year period in the run-up to the Index Date&#46; Only data in the Observation Window was used to represent the patient during model training&#44; validation&#44; and testing&#46;</p><elsevierMultimedia ident="fig0001"></elsevierMultimedia><p id="para0017" class="elsevierStylePara elsevierViewall">Patient features used in predictive modelling are shown in <a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a>&#46; For each patient&#44; two feature vector representations were generated&#46; The first representation consisted of static features &#8211; age&#44; gender&#44; and ethnicity&#46; The second representation reflected dynamic features associated with inpatient and outpatient activity over a five-year period of the Observation Window&#46; This temporal input vector was discretized into twelve exponentially increasing time bins&#44; such that the most recent time points were assigned to the shortest time bin&#46; If a feature &#40;e&#46;g&#46;&#44; ICD-10 codes&#41; within an Observation Window contained multiple values&#44; the most frequent value was retained&#46; In cases where a numerical feature &#40;e&#46;g&#46;&#44; BMI&#41; contained multiple entries within one observation window&#44; an average was calculated&#46; Missing values were filled by forward propagation&#46;</p><elsevierMultimedia ident="tbl0001"></elsevierMultimedia><p id="para0018" class="elsevierStylePara elsevierViewall">Numerical data was scaled to a range between 0 and 1&#44; whilst categorical data was represented as 32-dimensional vectors of a large pre-trained language model trained on <span class="elsevierStyleItalic">n</span> &#61; 2067&#44;531 full text PubMed articles totalling <span class="elsevierStyleItalic">n</span> &#61; 224&#44;427&#44;218 sentences &#91;<a class="elsevierStyleCrossRef" href="#bib0033">33</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0034">34</a>&#93;&#46;</p></span><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3</span><span class="elsevierStyleSectionTitle" id="cesectitle0012">Model training</span><p id="para0019" class="elsevierStylePara elsevierViewall">Given significant variation in length and density of patient records &#40;e&#46;g&#46;&#44; vital sign measurements in an intensive care unit vs&#46; outpatient clinic&#41;&#44; we formulated a simple Transformer architecture with multi-head attention &#91;<a class="elsevierStyleCrossRef" href="#bib0035">35</a>&#93;&#44; to take advantage of such data&#46;</p><p id="para0020" class="elsevierStylePara elsevierViewall">Input layers of the Transformer network were adjusted to concurrently use time-invariant and time-dependent features&#46; Multiple inputs were concatenated along a horizontal axis and passed to four transformer encoder blocks with multi-head attention&#46; Four attention heads were used with head size fixed at 256&#46; The classification head of the network consisted of a global average pooling layer&#44; followed by a dense layer with rectified linear unit &#91;<a class="elsevierStyleCrossRef" href="#bib0036">36</a>&#93; activation and a dropout layer&#46; A softmax activation function was applied to the final dense layer&#46;</p><p id="para0021" class="elsevierStylePara elsevierViewall">The number of neurons in the penultimate dense layer and the dropout rate were tuneable hyperparameters optimized during training using the Hyperband algorithm &#91;<a class="elsevierStyleCrossRef" href="#bib0037">37</a>&#93;&#44; with the best set of parameters corresponding to the lowest sparse categorical cross-entropy loss on the validation set&#46; The number of neurons was selected from the range of &#91;32&#44; 512&#93;&#44; and the dropout rate took values from the range &#91;0&#44; 0&#46;2&#93;&#46;</p><p id="para0022" class="elsevierStylePara elsevierViewall">Training was performed with batch size of 512 using an Adam optimizer with a learning rate of 1&#8201; &#215; &#8201;10<span class="elsevierStyleSup">&#8722;4</span> while minimizing the categorical cross-entropy loss&#46;</p><p id="para0023" class="elsevierStylePara elsevierViewall">The network was trained to output probabilities of mortality following 12-&#44; 24-&#44; and 36-month prediction windows&#46; Training was terminated early if validation loss did not improve after ten consecutive epochs&#46;</p></span><span id="sec0006" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;4</span><span class="elsevierStyleSectionTitle" id="cesectitle0013">Statistical analysis</span><p id="para0024" class="elsevierStylePara elsevierViewall">Model performance was assessed using the area under the receiver operating characteristic &#40;AUROC&#41; curve&#44; overall accuracy&#44; sensitivity&#44; specificity&#44; and positive predictive value &#40;PPV&#41;&#46; For AUROC measures&#44; 95 &#37; confidence intervals &#40;CIs&#41; were calculated empirically using 2000 bootstrap samples&#46; CIs for sensitivity&#44; specificity&#44; and positive predictive value are exact Clopper-Pearson CIs&#46; Patient demographics were compared across the training&#47;validation&#44; internal testing&#44; and clinical evaluation sets using ANOVA for continuous variables and Chi-square for categorical variables&#46; <span class="elsevierStyleItalic">p</span>-values &#60; 0&#46;05 were considered as statistically significant&#46;</p><p id="para0025" class="elsevierStylePara elsevierViewall">Dimensionality reduction was performed using the Ivis algorithm &#91;<a class="elsevierStyleCrossRef" href="#bib0038">38</a>&#93;&#46; Briefly&#44; prior to analysis&#44; categorical variables were one-hot encoded&#44; whilst numerical variables were scaled to values between 0 and 1&#46; The dataset was reduced to two components using the &#8216;maaten&#8217; twin neural network architecture and default Ivis hyperparameter values&#46; To identify the salient features captured by the Transformer model&#44; we calculated the coefficient of determination &#40;R<span class="elsevierStyleSup">2</span>&#41; between low-dimensional representations of the model global average pooling layer and training set features&#46; Where categorical features were used&#44; their numerical representation was extracted from the model&#39;s feature embedding layer&#46;</p><p id="para0026" class="elsevierStylePara elsevierViewall">Model probabilities were evaluated using the reliability diagram &#91;<a class="elsevierStyleCrossRef" href="#bib0039">39</a>&#93; and the calibration&#95;curve function in the scikit-learn library &#91;<a class="elsevierStyleCrossRef" href="#bib0040">40</a>&#93;&#46; Predicted probabilities were binned into ten discrete intervals&#44; and the mean predicted probability and the true frequency of the positive class were plotted for each interval&#46;</p><p id="para0027" class="elsevierStylePara elsevierViewall">All statistical tests were carried out using the SciPy module &#40;version 1&#46;7&#46;3&#41; for Python &#40;version 3&#46;9&#46;14&#41;&#46;</p></span><span id="sec0007" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;5</span><span class="elsevierStyleSectionTitle" id="cesectitle0014">Ethical statement</span><p id="para0028" class="elsevierStylePara elsevierViewall">Unified transparent approval for unconsented data inclusion in the multimodal pan-Scotland SteatoSITE database &#91;<a class="elsevierStyleCrossRef" href="#bib0030">30</a>&#93; was provided by the West of Scotland Research Ethics Committee 4 &#40;Reference&#58; 20&#47;WS&#47;0002&#59; 18th February 2020&#41;&#44; Public Benefit and Privacy Panel for Health and Social Care &#40;PBPP&#59; Reference&#58; 1819&#8211;0091&#59; 4th June 2021&#41;&#44; Institutional Research &#38; Development departments and Caldicott Guardians&#46; Delegated research and ethics approvals for the validation cohort study were granted by the Local and Advisory Committee at NHS Greater Glasgow and Clyde &#40;NHS GG&#38;C&#41;&#46; The cohort and de-identified linked data were prepared by the West of Scotland Safe Haven at NHS GG&#38;C&#46; In Scotland&#44; patient consent is not required where routinely collected patient data are used for research purposes through an approved Safe Haven&#46; For that reason&#44; informed consent was not required and was not sought&#46; All research was conducted following both the Declarations of Helsinki &#40;2013&#41; and Istanbul &#40;2018&#41;&#44; and Good Clinical Practice principles&#46; This study was conducted and reported in accordance with the TRIPOD &#40;Transparent Reporting of a multivariable prediction model for Individual Prediction or Diagnosis&#41; guidelines&#46;</p></span></span><span id="sec0008" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="cesectitle0015">Results</span><span id="sec0009" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;1</span><span class="elsevierStyleSectionTitle" id="cesectitle0016">Training and testing dataset characteristics</span><p id="para0029" class="elsevierStylePara elsevierViewall">Demographic and phenotypic characteristics of the training and testing cohorts are shown in <a class="elsevierStyleCrossRef" href="#tbl0002">Table 2</a>&#46; Patient age in the training set was significantly younger than the testing set &#40;two-tailed unpaired <span class="elsevierStyleItalic">t-</span>test&#44; <span class="elsevierStyleItalic">p</span> &#61; 0&#46;0001&#41;&#44; whilst there were no significant differences in BMI &#40;two-tailed unpaired <span class="elsevierStyleItalic">t</span>-test&#44; <span class="elsevierStyleItalic">p</span> &#61; 0&#46;76&#41; or the frequencies of gender or ethnicity distributions &#40;chi-square&#44; <span class="elsevierStyleItalic">p</span> &#61; 0&#46;12&#8211;0&#46;16&#41;&#46;</p><elsevierMultimedia ident="tbl0002"></elsevierMultimedia><p id="para0030" class="elsevierStylePara elsevierViewall">The most common cause of death in both the training and testing sets was extra-hepatic cancer &#40;<a class="elsevierStyleCrossRef" href="#tbl0003">Table 3</a>&#41;&#46; In the training set &#40;SteatoSITE&#41;&#44; liver-related mortality &#40;cirrhosis and HCC&#41; was the second most common cause of death&#44; followed by cardiovascular deaths&#44; similar to the findings of a large nationwide cohort study of over 10&#44;000 patients with biopsy-confirmed NAFLD &#91;<a class="elsevierStyleCrossRef" href="#bib0032">32</a>&#93;&#46; In contrast&#44; in the testing set &#40;GG&#38;C non-biopsy cohort&#41; cardiovascular deaths were higher&#46; This likely reflects the different composition of the respective populations&#46; One was a secondary care cohort based on clinically indicated tissue sampling &#40;biopsy&#44; resection&#44; or explant&#41;&#44; so there is inherent spectrum bias towards cases with more severe liver disease&#44; whereas the other represents a more generalizable MASLD cohort&#44; reflected in the observed cause of death frequencies that are more consistent with data from other community-diagnosed population studies &#91;<a class="elsevierStyleCrossRef" href="#bib0041">41</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0042">42</a>&#93;&#46; Notably&#44; a substantial number of deaths did not fall into any of these categories and were classified as &#8216;other&#8217;&#46;</p><elsevierMultimedia ident="tbl0003"></elsevierMultimedia></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;2</span><span class="elsevierStyleSectionTitle" id="cesectitle0017">Prediction of all-cause mortality</span><p id="para0031" class="elsevierStylePara elsevierViewall">The Transformer neural network was trained and validated using five-fold cross-validation in the SteatoSITE dataset&#46; Dimensionality reduction of the global average pooling layer confirmed the model&#39;s propensity to learn the target class &#40;<a class="elsevierStyleCrossRef" href="#fig0002">Fig&#46; 2</a>A&#41;&#46; The model achieved an AUROC of 0&#46;90 &#40;95 &#37; CI&#58; 0&#46;86&#8211;0&#46;94&#41;&#44; 0&#46;85 &#40;95 &#37; CI&#58; 0&#46;79&#8211;0&#46;90&#41;&#44; and 0&#46;73 &#40;95 &#37; CI&#58; 0&#46;69&#8211;0&#46;79&#41; for the prediction of 12-&#44; 24&#44; and 36-month mortality&#44; respectively&#46;</p><elsevierMultimedia ident="fig0002"></elsevierMultimedia><p id="para0032" class="elsevierStylePara elsevierViewall">Binarizing predicted cases and controls using an operating point of probability of mortality &#8805; 50 &#37;&#44; resulted in sensitivity of 64 &#37;-82 &#37; &#40;95 &#37; CI&#58; 69 &#37;-94 &#37;&#41; specificity of 75 &#37;-92 &#37; &#40;95 &#37; CI 72 &#37;-95 &#37;&#41;&#44; and PPV of 94&#8211;98 &#37; &#40;95 &#37; CI&#58; 91 &#37;-100 &#37;&#41;&#46; Model probabilities were well calibrated&#44; with a Pearson&#39;s R2-values of 0&#46;94&#8211;0&#46;99 &#40;two-sided <span class="elsevierStyleItalic">p</span>-value &#61; 9&#46;9 &#61; 10<span class="elsevierStyleSup">&#8722;4</span> &#8211; 8&#46;3 &#61; 10<span class="elsevierStyleSup">&#8722;2</span>&#44; <a class="elsevierStyleCrossRef" href="#fig0002">Fig&#46; 2</a>B&#41;&#46;</p><p id="para0033" class="elsevierStylePara elsevierViewall">Model performance generalized well to the out-of-sample dataset&#44; with AUROCs of 0&#46;86 &#40;95 &#37; CI&#58; 0&#46;85&#8211;0&#46;90&#41;&#44; 0&#46;80 &#40;95 &#37; CI&#58; 0&#46;79&#8211;0&#46;88&#41;&#44; and 0&#46;70 &#40;95 &#37; CI&#58; 0&#46;67&#8211;0&#46;74&#41; for prediction of mortality after 12&#44; 24&#44; and 36 months respectively&#46; Binarizing predicted cases and controls using an operating point of probability of mortality &#8805; 50 &#37;&#44; resulted in sensitivity of 69&#8211;70 &#37; &#40;95 &#37; CI&#58; 67 &#37;-75 &#37;&#41;&#44; specificity of 96&#8211;97 &#37; &#40;95 &#37; CI 94 &#37;-98 &#37;&#41;&#44; and PPV of 75 &#37;-77 &#37; &#40;95 &#37; CI&#58; 74 &#37;-81 &#37;&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0004">Table 4</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0004"></elsevierMultimedia><p id="para0034" class="elsevierStylePara elsevierViewall">Coefficients of Determination &#40;see Methods&#41; were calculated for every input feature&#46; Features that correlated the most with the global average pooling layer of the model were serum albumin &#40;R<span class="elsevierStyleSup">2</span> &#61; 0&#46;89&#41;&#44; estimated glomerular filtration rate &#40;R<span class="elsevierStyleSup">2</span> &#61; 0&#46;75&#41;&#44; and aspartate aminotransferase &#40;AST&#41; levels &#40;R<span class="elsevierStyleSup">2</span> &#61; 0&#46;67&#41;&#44; as well as BMI &#40;R<span class="elsevierStyleSup">2</span> &#61; 0&#46;63&#41;&#44; age at index date &#40;R<span class="elsevierStyleSup">2</span> &#61; 0&#46;58&#41;&#44; and systolic blood pressure &#40;R<span class="elsevierStyleSup">2</span> &#61; 0&#46;55&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0002">Fig&#46; 2</a>C&#41;&#46;</p><p id="para0035" class="elsevierStylePara elsevierViewall">Model misclassifications in the out-of-sample testing set were interpretable&#46; For example&#44; at 36-month probability of mortality &#8805; 50 &#37;&#44; the model identified n &#61; 49 false positive cases&#46; Of these&#44; <span class="elsevierStyleItalic">n</span> &#61; 11 patients &#40;22&#46;4 &#37;&#41; and <span class="elsevierStyleItalic">n</span> &#61; 13 &#40;26&#46;5 &#37;&#41; had recent diagnoses &#40;within twelve months&#41; of &#8216;acute myocardial infarction&#8217; &#40;I21&#46;9&#41; and &#8216;atherosclerotic heart disease of native coronary artery&#8217; &#40;I25&#46;1&#41;&#46; Furthermore&#44; <span class="elsevierStyleItalic">n</span> &#61; 22 &#40;44&#46;9 &#37;&#41;&#44; <span class="elsevierStyleItalic">n</span> &#61; 9 &#40;18&#46;3 &#37;&#41;&#44; and <span class="elsevierStyleItalic">n</span> &#61; 16 &#40;32&#46;6 &#37;&#41; patients had at least a three-year history of lipid regulators&#44; beta adrenoreceptor blockers&#44; and antiplatelet drug usage&#46; Finally&#44; <span class="elsevierStyleItalic">n</span> &#61; 25 patients &#40;51 &#37;&#41; had in-patient stays under Cardiology services as their primary specialty&#46;</p><p id="para0036" class="elsevierStylePara elsevierViewall">Conversely&#44; at probability of 36-month mortality &#8805; 50 &#37;&#44; the model identified n &#61; 38 false negative cases&#46; Of these&#44; the most common diagnoses upon discharge over a ten-year Observation Window were&#44; were &#8216;urinary tract infection&#44; site unspecified&#8217; &#40;<span class="elsevierStyleItalic">n</span> &#61; 27 patients &#91;71&#46;1 &#37;&#93;&#44; N39&#46;0&#41; and &#8216;unspecified acute lower respiratory infection&#8217; &#40;<span class="elsevierStyleItalic">n</span> &#61; 22 patients &#91;57&#46;9 &#37;&#93;&#44; J22&#46;X&#41;&#46; The most common primary specialty amongst the false negative cases was General Medicine &#40;<span class="elsevierStyleItalic">n</span> &#61; 35 patients&#44; 92&#46;1 &#37;&#41; and General Surgery &#40;<span class="elsevierStyleItalic">n</span> &#61; 24 patients&#44; 63&#46;1 &#37;&#41;&#46; Finally&#44; the most frequently prescribed medication classes were non-opioid analgesics &#40;<span class="elsevierStyleItalic">n</span> &#61; 35 patients&#44; 92&#46;1 &#37;&#41; and antidiabetic drugs &#40;<span class="elsevierStyleItalic">n</span> &#61; 19 patients&#44; 50&#46;0 &#37;&#41;&#46;</p></span></span><span id="sec0011" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="cesectitle0018">Discussion</span><p id="para0037" class="elsevierStylePara elsevierViewall">MASLD is a global public health problem with multisystem and multidisciplinary implications &#91;<a class="elsevierStyleCrossRef" href="#bib0043">43</a>&#93;&#46; However&#44; techniques for accurately predicting the risk of adverse clinical outcomes in patients with MASLD&#44; such as liver biopsy &#91;<a class="elsevierStyleCrossRef" href="#bib0044">44</a>&#93; or dynamic changes in imaging measurements &#91;<a class="elsevierStyleCrossRef" href="#bib0045">45</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0046">46</a>&#93;&#44; rely on patient engagement and healthcare resource utilization&#46; Here&#44; we demonstrate that a simple Transformer neural network model&#44; trained on routinely collected secondary care data&#44; produced well-calibrated probabilities and achieved good discriminatory power in an out-of-sample dataset within a long &#40;up to 3-year&#41; predictive window&#44; with AUROC of 0&#46;70&#8211;0&#46;86 &#40;95 &#37; CI&#58; 0&#46;67&#8211;0&#46;90&#41; for all-cause mortality&#46;</p><p id="para0038" class="elsevierStylePara elsevierViewall">The training and out-of-sample testing sets were comparable in terms of gender and ethnicity distributions&#44; as well as BMI ranges&#46; Patients in the training set were significantly younger than the testing set &#40;55 <span class="elsevierStyleItalic">vs</span>&#46; 66&#46;3 years&#44; <a class="elsevierStyleCrossRef" href="#tbl0002">Table 2</a>&#41;&#46; However&#44; this discrepancy did not adversely influence model performance&#46; Notably&#44; the cardiovascular-specific cause of death was significantly more prevalent in the testing set&#44; compared to the training cohort &#40;23&#46;9 &#37; vs<span class="elsevierStyleItalic">&#46;</span> 12&#46;9 &#37;&#41;&#44; which may contribute to the increased overall prevalence of deaths in the out-of-sample set &#40;23&#46;94 &#37; vs<span class="elsevierStyleItalic">&#46;</span> 37&#46;12 &#37;&#41;&#46;</p><p id="para0039" class="elsevierStylePara elsevierViewall">The mortality classifier used in this study is a Transformer neural network &#40;TNN&#41; &#91;<a class="elsevierStyleCrossRef" href="#bib0035">35</a>&#93;&#46; Traditionally&#44; the Transformer architecture was extensively applied to natural language processing&#44; achieving state-of-the-art performance in text annotation &#91;<a class="elsevierStyleCrossRef" href="#bib0033">33</a>&#93;&#44; named entity recognition &#91;<a class="elsevierStyleCrossRef" href="#bib0047">47</a>&#93;&#44; and representation learning &#91;<a class="elsevierStyleCrossRef" href="#bib0048">48</a>&#93;&#46; More recently&#44; the utility of the Transformer architecture was explored in longitudinal EHRs&#44; demonstrating a striking capacity to parse heterogeneous data sequences and predict multiple clinical trajectories &#91;<a class="elsevierStyleCrossRef" href="#bib0049">49</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0050">50</a>&#93;&#44; considerably outperforming conventional ML techniques&#46; The propensity of this technique to handle large volumes of relatively noisy data&#44; including errors in labels&#44; as well as large numbers of input variables &#91;<a class="elsevierStyleCrossRef" href="#bib0018">18</a>&#93;&#44; makes it an attractive tool for interrogation of real-world EHRs&#46;</p><p id="para0040" class="elsevierStylePara elsevierViewall">Machine learning models have traditionally targeted the detection of MASLD in the general population using routinely collected medical records&#46; For example&#44; a simple coarse trees model that utilized fasting C-peptide levels and waist circumference identified MASLD with 74&#46;9 &#37; accuracy in <span class="elsevierStyleItalic">n</span> &#61; 3235 individuals &#91;<a class="elsevierStyleCrossRef" href="#bib0026">26</a>&#93;&#46; Similarly&#44; large-scale analyses &#40;n &#61; 1016&#8211;73&#44;190 patients&#41; of EHRs in secondary care settings predicted MASLD with AUROC 0&#46;83&#8211;0&#46;92 &#91;<a class="elsevierStyleCrossRef" href="#bib0051">51</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0052">52</a>&#93;&#46;</p><p id="para0041" class="elsevierStylePara elsevierViewall">The assumption behind current diagnostic algorithms in MASLD is that liver disease is the principal threat for these patients&#44; whilst the weight of evidence indicates it is not &#91;<a class="elsevierStyleCrossRef" href="#bib0053">53</a>&#93;&#46; Leveraging large&#44; diverse&#44; multidimensional datasets in MASLD and applying sophisticated methods such as AI&#47;ML tools or multi-state modelling &#91;<a class="elsevierStyleCrossRef" href="#bib0054">54</a>&#93; will elucidate novel subphenotypes with disease trajectories reflecting variable susceptibility to liver-related and&#47;or non-liver-related outcomes&#46; Additionally&#44; the availability of &#8216;upstream&#8217; risk stratification tools that consider the whole patient history could assist in developing a new paradigm for community-based prognostication in MASLD that captures key demographic influences &#40;such as age&#44; gender&#44; ethnicity&#44; and deprivation index&#41; and embraces comorbidity and polypharmacy&#46; This strategy aligns with a growing shift in government policy in many countries towards a more preventative and anticipatory approach to the management of long-term conditions such as MASLD&#59; the value of which is maximized when it is targeted at patients who are most likely to benefit&#46;</p><p id="para0042" class="elsevierStylePara elsevierViewall">In this work we achieve a good balance between prediction window length &#40;one to three years&#41; and model performance &#40;out-of-sample AUROC 0&#46;70&#8211;0&#46;86&#41;&#46; Indeed&#44; shorter prediction windows provide limited therapeutic benefit&#44; with underlying disease mechanisms becoming less modifiable&#44; whilst longer prediction windows may result in many false positives&#44; rendering proactive therapeutic or lifestyle intervention less practicable &#91;<a class="elsevierStyleCrossRef" href="#bib0055">55</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0056">56</a>&#93;&#46;</p><p id="para0043" class="elsevierStylePara elsevierViewall">We utilized a data-driven strategy to delineate the salient features captured by our model by computing the coefficient of determination &#40;R<span class="elsevierStyleSup">2</span>&#41; between low-dimensional representations of the model global average pooling layer and input features&#46; Although several algorithms exist to explain black box models &#91;<a class="elsevierStyleCrossRef" href="#bib0057">57</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0058">58</a>&#93;&#44; they are limited to lower-dimensional tabular data&#46; Our approach&#44; validated in medical imaging &#91;<a class="elsevierStyleCrossRef" href="#bib0059">59</a>&#93;&#44; attempts to explain features captured within the unstructured temporal information&#46; Variability in albumin levels and eGFR over the ten-year observation window accounted for 90 &#37; and 75 &#37; of the variance in the model&#39;s global average pooling embedding layer&#44; respectively&#46; Recently&#44; a multicenter study &#40;<span class="elsevierStyleItalic">n</span> &#61; 229 patients from 22 hospitals&#41; demonstrated that an annual decline in serum albumin concentration in patients with MASLD is associated with adverse events&#44; including gastroesophageal varices leading to rupture or requiring preventive intervention&#44; hepatic failure leading to hepatic encephalopathy&#44; HCC&#44; other organ malignancy&#44; and cardiovascular events &#91;<a class="elsevierStyleCrossRef" href="#bib0060">60</a>&#93;&#46; Similarly&#44; in <span class="elsevierStyleItalic">n</span> &#61; 18&#44;073 UK Biobank participants identified to have CKD&#44; MASLD was associated with an increased risk of cardiovascular events&#44; all-cause mortality&#44; and end-stage kidney disease &#91;<a class="elsevierStyleCrossRef" href="#bib0061">61</a>&#93;&#46; These studies corroborate the utility of the coefficient of determination to identify important learnt features in complex black box models&#46;</p><p id="para0044" class="elsevierStylePara elsevierViewall">Our Transformer model presents several advantages&#46; First&#44; it was trained on a diverse population using routinely collected EHRs on a national scale&#44; covering the full MASLD severity spectrum&#46; This offers a robust inclusion criterion for a population-level risk stratification algorithm&#46; Second&#44; model probabilities were well calibrated and generalized well to an out-of-sample population&#46; Finally&#44; an accurate inference at three-year prediction window resolution offers an opportunity for a timely&#44; low-cost preventative intervention in the general population&#46;</p><p id="para0045" class="elsevierStylePara elsevierViewall">Our study also had limitations&#46; First&#44; we chose all-cause mortality as our initial clinical outcome of interest&#44; as this is a hard endpoint that is free from bias&#46; Future work will extend our approach to liver-specific outcomes&#46; Second&#44; the training set was relatively small &#40;<span class="elsevierStyleItalic">n</span> &#61; 940 patients&#41; and may not represent the full breadth of clinical activity in MASLD patients&#44; although all stages of the disease were equally represented&#46; Furthermore&#44; larger evaluation cohorts would provide insight into model performance and potential biases across different strata of the population &#40;e&#46;g&#46;&#44; ethnicity&#44; age groups&#44; and deprivation indices&#41; &#91;<a class="elsevierStyleCrossRef" href="#bib0062">62</a>&#93;&#46; This presents an urgent requirement to validate model generalizability in other systems outside Scotland&#46; This should be feasible due to the routine availability of model features and is currently our primary focus of research&#46; Next&#44; the retrospective nature of this study resulted in a level of class balance that may not represent real-world prevalence&#46; Therefore&#44; any future validation should involve a prospectively selected cohort of patients&#46; Finally&#44; despite our work on coefficient of determination&#44; the black-box nature and the dimensionality of training data make interpretation of our model unintuitive&#46; This can pose a challenge to clinical implementation&#46;</p></span><span id="sec0012" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5</span><span class="elsevierStyleSectionTitle" id="cesectitle0019">Conclusions</span><p id="para0046" class="elsevierStylePara elsevierViewall">In conclusion&#44; we show that a simple Transformer model utilizing routinely collected EHRs may offer a robust tool for community-based risk stratification of MASLD patients at an increased risk of all-cause mortality&#46; Integration of such models into health and social care systems could assist primary care physicians in the targeting of anticipatory interventions at the individual patient level&#44; refine secondary care referral pathways&#44; and assist more broadly in service planning&#46; Future work will require a prospective validation study&#44; which would allow for evaluation of the algorithm when exposed to real-world class distributions&#44; assessing its effect on workflow safety and operational efficiency&#46;</p></span><span id="sec0013" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0020">Funding</span><p id="para0047" class="elsevierStylePara elsevierViewall">This work was funded by a grant from <span class="elsevierStyleGrantSponsor" id="gs0001">Innovate UK EUREKA</span> &#40;Reference&#58; <span class="elsevierStyleGrantNumber" refid="gs0001">105976</span>&#41; and by Bering Limited&#46; For the purpose of open access&#44; the author has applied a creative commons attribution &#40;CC BY&#41; licence to any author-accepted manuscript version arising&#46;</p></span><span id="sec0014" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0021">Author contributions</span><p id="para0048" class="elsevierStylePara elsevierViewall">ID&#44; JAF&#44; TJK&#58; conception and design&#44; acquisition of data&#59; ID&#44; BS&#44; JAF&#44; TJK&#44; IAR&#58; analysis and interpretation of data&#59; ID&#44; JAF&#58; drafting the article&#59; TJK&#44; IAR&#58; revising the article critically for important intellectual content&#59; all authors&#58; approval of the final manuscript version&#46; &#42;ID and JAF are co-corresponding authors&#46;</p></span></span>"
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    "fechaRecibido" => "2024-06-10"
    "fechaAceptado" => "2024-06-13"
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        0 => array:4 [
          "clase" => "keyword"
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          "palabras" => array:5 [
            0 => "Metabolic dysfunction-associated steatotic liver disease"
            1 => "Electronic health records"
            2 => "Artificial intelligence"
            3 => "Deep Learning"
            4 => "Prognostic model"
          ]
        ]
        1 => array:4 [
          "clase" => "abr"
          "titulo" => "Abbreviations"
          "identificador" => "xpalclavsec1868225"
          "palabras" => array:14 [
            0 => "A&#38;E"
            1 => "AI"
            2 => "BMI"
            3 => "CKD"
            4 => "DNN"
            5 => "EHR"
            6 => "HCC"
            7 => "ICD"
            8 => "MASLD"
            9 => "ML"
            10 => "NAFLD"
            11 => "NASH"
            12 => "OPCS-4"
            13 => "TNN"
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        "resumen" => "<span id="abss0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0002">Introduction and Objectives</span><p id="spara007" class="elsevierStyleSimplePara elsevierViewall">Despite the huge clinical burden of MASLD&#44; validated tools for early risk stratification are lacking&#44; and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty&#46; We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model&#46;</p></span> <span id="abss0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0003">Patients and Methods</span><p id="spara008" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">n</span> &#61; 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction&#46; Patient timelines&#44; spanning 12 years&#44; were fully-annotated with demographic&#47;clinical characteristics&#44; ICD-9 and -10 codes&#44; blood test results&#44; prescribing data&#44; and secondary care activity&#46; A Transformer neural network &#40;TNN&#41; was trained to output concomitant probabilities of 12-&#44; 24-&#44; and 36-month all-cause mortality&#46; In-sample performance was assessed using 5-fold cross-validation&#46; Out-of-sample performance was assessed in an independent set of <span class="elsevierStyleItalic">n</span> &#61; 528 MASLD patients&#46;</p></span> <span id="abss0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0004">Results</span><p id="spara009" class="elsevierStyleSimplePara elsevierViewall">In-sample model performance achieved AUROC curve 0&#46;74&#8211;0&#46;90 &#40;95 &#37; CI&#58; 0&#46;72&#8211;0&#46;94&#41;&#44; sensitivity 64 &#37;-82 &#37;&#44; specificity 75 &#37;&#8211;92 &#37; and Positive Predictive Value &#40;PPV&#41; 94 &#37;-98 &#37;&#46; Out-of-sample model validation had AUROC 0&#46;70&#8211;0&#46;86 &#40;95 &#37; CI&#58; 0&#46;67&#8211;0&#46;90&#41;&#44; sensitivity 69 &#37;&#8211;70 &#37;&#44; specificity 96 &#37;&#8211;97 &#37; and PPV 75 &#37;&#8211;77 &#37;&#46; Key predictive factors&#44; identified using coefficients of determination&#44; were age&#44; presence of type 2 diabetes&#44; and history of hospital admissions with length of stay &#62;14 days&#46;</p></span> <span id="abss0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0005">Conclusions</span><p id="spara010" class="elsevierStyleSimplePara elsevierViewall">A TNN&#44; applied to routinely-collected longitudinal electronic health records&#44; achieved good performance in prediction of 12-&#44; 24-&#44; and 36-month all-cause mortality in patients with MASLD&#46; Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions&#46;</p></span>"
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          "en" => "<p id="spara001" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleBold">Schematic representation of an EHR vector&#46;</span> The patient&#39;s timeline is represented by horizontal arrows and each data point is depicted by colour-coded tokens&#46; Predictive models were trained on the data in the Observation Window &#40;10 years&#41;&#44; whilst a binary outcome of all-cause mortality was used as a ground truth&#46; A&#38;E&#44; emergency department&#59; U&#38;Es&#44; urea and electrolytes&#59; FBC&#44; full blood count&#44; LFT&#44; liver function tests&#59; ACE inhibitor&#44; angiotensin-converting enzyme inhibitor&#59; BMI&#44; body mass index&#46;</p>"
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          "en" => "<p id="spara002" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleBold">Transformer neural network performance in prediction of all-cause mortality in the SteatoSITE dataset&#46; &#40;A&#41;</span> Scatterplot shows two-dimensional twin neural network &#40;Ivis&#41; embedding of the global average pooling layer values in the trained transformer neural network&#46; Each point represents a single patient in the testing set&#46; Blue and orange colours represent the presence and absence of all-cause mortality in a 12&#8211;36 month predictive window respectively&#46; &#40;<span class="elsevierStyleBold">B&#41;</span> Calibration plots demonstrating the relationship between average SteatoSITE cohort mortality probabilities and proportion of true positives within each probability bin&#46; Green&#44; blue&#44; and orange lines reflect model outputs for 12-&#44; 24-&#44; 36-month predictive windows&#46; <span class="elsevierStyleBold">C&#41;</span> Bar plot showing model input features and their respective coefficient of determination &#40;R<span class="elsevierStyleSup">2</span>&#41; values&#46; Values reflect variance within the global average pooling layer explained by each feature&#46; ALB&#44; albumin&#58; EGFR&#44; estimated glomerular filtration rate&#59; AST&#44; aspartate aminotransferase&#59; BMI&#44; body mass index&#59; ALT&#44; alanine aminotransferase&#59; BP&#44; blood pressure&#59; ALP&#44; alkaline phosphatase&#59; CREA&#44; creatinine&#59; HB&#44; hemoglobin&#59; DIAG1&#44; code for main diagnosis&#59; DIAG2&#8211;4&#44; codes for other diagnosis&#59; OP1B&#44; code for approach site&#47;laterality of main procedure&#59; SPEC&#44; code for speciality&#59; A&#38;E&#44; Accident and Emergency&#59; BNF&#44; British National Formulary&#59; K&#44; potassium&#59; LYM&#44; lymphocytes&#59; NEUT&#44; neutrophils&#46;</p>"
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                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleBold">Data Type</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleBold">Description</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="top">Age&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="top">Patient age at Index Date&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="top">Gender&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="top">Medications&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="top">British National Formulary &#40;BNF&#41; Subsection codes&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">Serum analytes&#58; urea&#44; estimated glomerular filtration rate &#40;eGFR&#41;&#44; creatinine&#44; sodium&#44; potassium&#44; hemoglobin&#44; neutrophils&#44; lymphocytes&#44; platelets&#44; total bilirubin&#44; alanine aminotransferase&#44; aspartate aminotransferase&#44; alkaline phosphatase&#44; gamma-glutamyltransferase&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Training Set&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><a name="en0023"></a><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Testing Set&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><a name="en0024"></a><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p-v</span>alue&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0025"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0026"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">55 &#40;&#43;&#47;- 13&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0027"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">66&#46;3 &#40;&#43;&#47;- 14&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0028"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;0001<a class="elsevierStyleCrossRef" href="#tb2fn1">&#42;</a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0029"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">Gender&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0030"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0031"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0032"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;16<a class="elsevierStyleCrossRef" href="#tb2fn2"><span class="elsevierStyleSup">&#8270;&#8270;</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0033"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>Male&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0034"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">55&#46;4 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0035"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">45 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0036"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0037"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>Female&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0038"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">44&#46;6 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0039"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">55 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0040"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0041"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">Ethnicity&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0042"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0043"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0044"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;12<a class="elsevierStyleCrossRef" href="#tb2fn2"><span class="elsevierStyleSup">&#8270;&#8270;</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0045"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>Asian&#44; Asian British&#44; Asian Welsh&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0046"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">2&#46;34 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0047"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">5&#46;58 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0048"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0049"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>Black&#44; Black British&#44; Black Welsh&#44; Caribbean or African&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0050"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;11 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0051"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0052"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0053"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>Mixed or Multiple&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0054"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0055"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;24 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0056"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0057"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>White&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0058"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">62&#46;98 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0059"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">59&#46;39 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0060"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0061"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>Other&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0062"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0063"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">5&#46;79 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0064"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0065"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top"><span class="elsevierStyleHsp" style=""></span>Unknown&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0066"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">34&#46;57&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0067"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">29 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0068"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0069"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">Type 2 Diabetes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0070"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">32 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0071"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">30 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0072"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0073"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">BMI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0074"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">32&#46;82 &#40;&#43;&#47;-7&#46;94&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0075"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">32&#46;95 &#40;&#43;&#47;-7&#46;96&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0076"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;76<a class="elsevierStyleCrossRef" href="#tb2fn2"><span class="elsevierStyleSup">&#8270;&#8270;</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0077"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">Number of Deaths&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0078"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">225 &#40;23&#46;94 &#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0079"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">196 &#40;37&#46;12 &#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0080"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
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                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0081"></a><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><a name="en0082"></a><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Training Set&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><a name="en0083"></a><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Testing Set&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0084"></a><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">Cardiovascular-specific &#40;ICD10&#58; I01-I99&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0085"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">18&#46;1 &#37; &#40;12&#46;9 &#37; including resections&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0086"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">23&#46;9 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0087"></a><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">Hepatocellular Carcinoma &#40;ICD10&#58; C22&#46;0&#44; C22&#46;9&#44; C22&#46;99&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0088"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">6&#46;3 &#37; &#40;9&#46;3 &#37; including resections&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0089"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">2&#46;5 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0090"></a><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">Cirrhosis-specific &#40;ICD10&#58; K74&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0091"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">23&#46;6 &#37; &#40;13&#46;3 &#37; including resections&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0092"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">2&#46;5 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0093"></a><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">Cancer-specific &#40;ICD10&#58; C00-C99&#44; excluding C22&#46;0&#44; C22&#46;9&#44; C22&#46;99&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0094"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">33&#46;1 &#37; &#40;52&#46;0 &#37; including resections&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0095"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">28&#46;5 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><a name="en0096"></a><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">Other causes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0097"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">18&#46;9 &#37; &#40;12&#46;4 &#37; including resections&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><a name="en0098"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">42&#46;6 &#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
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Original language: English
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