Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

Abstract

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<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group. 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.

The use of natural language processing in palliative care research: A scoping review

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

“…These applications offer the possibility of several toolkits for natural language processing and also the possibility to develop codes based on the researchers' needs. Another interesting finding was that many of the studies used a combination of more than one 27 Elhazmi et al, 33 Ganguli et al, 36 George et al, 37 Hu et al, 44 Kehl et al, 48 Laios et al, 50 Lin et al, 57 Manz et al, 66 Agarwal et al, 74 Santos et al, 77 Sung et al, 82 Ye et al 93 Assessment of the impact of interventions 9 (10.9) Ando et al, 22 Greer et al, 42 Lakin et al, 51 Lefèvre et al, 56 Macieira et al, 64 Santarpia et al, 76 Steiner et al, 80 Udelsman et al, 87 Uyeda et al 89 Social and spiritual health 8 (9.7) Gray et al, 40 Johnson et al, 46,47 Masukawa et al, 67 Yoon et al, 94 Ando et al, 96 Ando et al 99 Topic identification 8 (9.7) Sarmet et al, 5 Ando et al, 21 Chan et al, 26 Davoudi et al, 29 Agaronnik et al, 52 Lucini et al, 61 Seale et al, 78 Wang et al 90 Advance care planning/EOL process measures/Code-status clarification/Goals of care documentation 8 (9.7)…”

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,[23][24][25][26][27]29,30,[33][34][35][36]40,[42][43][44][45][46][48][49][50][51][52]54,55,[57][58][59][60][61][62][63][64][66][67][68][70][71][72][73]75,[79][80][81][82][83][84][85][86][87][88][89]…”

Association between liver stiffness measurement by transient elastography and chronic kidney disease

“…And other studies also reveal that patients with severe liver diseases have abnormal creatinine and BUN outcomes. [ 38 , 39 ] However, there are some factors that would affect the evaluation performance for the liver stiffness by TE, such as the number of measurements, liver volumes, patient's conditions such as overweight or obesity or other complications as well as the fibrosis stage and experience of operators. Currently, it is generally agreed that 3 measurements are sufficient to obtain consistent results for assessing liver fibrosis.…”

Artificial Intelligence in Healthcare: Lost In Translation?

“…In order to successfully deploy AI in healthcare solutions, honest and repeated testing of the internal and external validity of the models is necessary at all stages in the development process [8]. External validity achieved by applying heterogenous data and appropriate clinical trials is crucial to avoid model bias and overblown clinical performance that will not hold in the clinical setting [8,44,45].…”