Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach. Issue 8 (31st May 2021)

• Record Type: Journal Article
• Authors: Yu, Cheng-Sheng; Chen, Yu-Da; Chang, Shy-Shin; Tang, Jui-Hsiang; Wu, Jenny L.; Lin, Chang-Hsien
• Is Part Of: European journal of gastroenterology & hepatology. Volume 33:Issue 8(2021)
• Page Start: 1117
• Page End: 1123
• Publication Date: 2021-05-31
• Subjects: data analysis -- ensemble learning -- medical informatics -- visualized clustering heatmap; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; Digestive organs -- Diseases; Liver -- Diseases; Periodicals; 616.33
• DOI: 10.1097/MEG.0000000000002169
• Languages: English
• ISSNs: 0954-691X

Abstract

Abstract : Objective: End-stage liver disease is a global public health problem with a high mortality rate. Early identification of people at risk of poor prognosis is fundamental for decision-making in clinical settings. This study created a machine learning prediction system that provides several related models with visualized graphs, including decision trees, ensemble learning and clustering, to predict mortality in patients with end-stage liver disease. Methods: A retrospective cohort study was conducted: the training data were from patients enrolled from January 2009 to December 2010 and followed up to December 2014; validation data were from patients enrolled from January 2015 to December 2016 and followed up to January 2019. Hospitalized patients with noncancer-related chronic liver disease were identified from the hospital's electrical medical records. Results: In traditional multivariable logistic regression and Cox proportional hazard model, prothrombin time of international normalized ratio, which was significant with P value = 0.002, odds ratio = 2.790 and hazard ratio 1.363. Besides, blood urea nitrogen and C-reactive protein were also significant, with P value <0.001 and 0.026. The area under the curve was 0.771 in the receiver operating characteristic curve. In machine learning, blood urea nitrogen and age were regarded as the primary factors for predicting mortality. Creatinine, prothrombin time of international normalized ratio and bilirubin were also significant mortality predictors. The area under the curve of the random forest and AdaBoost was 0.838 and 0.792. Conclusion: The machine learning techniques provided a comprehensive assessment of patient conditions; it could help physicians make an accurate diagnosis of chronic liver disease and improve healthcare management.