Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study
| Autor: | Li Chuan Chen, Cheng Sheng Yu, Shy Shin Chang, Jui Hsiang Tang, Yu Jiun Lin, Jenny L. Wu, Ray Jade Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty Palliative care data analysis Computer applications to medicine. Medical informatics noncancer-related end-stage liver disease R858-859.7 Health Informatics 03 medical and health sciences 0302 clinical medicine Health Information Management medical information system Acute care medicine Original Paper Receiver operating characteristic Proportional hazards model business.industry Mortality rate Medical record Hazard ratio Retrospective cohort study visualized clustering heatmap 030104 developmental biology machine learning Emergency medicine ensemble learning 030211 gastroenterology & hepatology business |
| Zdroj: | JMIR Medical Informatics, Vol 8, Iss 10, p e24305 (2020) JMIR Medical Informatics |
| ISSN: | 2291-9694 |
| Popis: | Background Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care. Objective We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed. Methods A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set. Results The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P Conclusions Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients. |
| Databáze: | OpenAIRE |
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