Open AccessPredicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning
Author(s) -
Frank Lien,
Hsin-Yao Wang,
Jang-Jih Lu,
YuChuan Wen,
TzongShi Chiueh
Publication year - 2020
Publication title -
medical care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.632
H-Index - 178
eISSN - 1537-1948
pISSN - 0025-7079
DOI - 10.1097/mlr.0000000000001421
Subject(s) - logistic regression , random forest , machine learning , decision tree , artificial intelligence , artificial neural network , naive bayes classifier , support vector machine , medicine , predictive modelling , receiver operating characteristic , computer science , statistics , mathematics
Clinical laboratories have traditionally used a single critical value for thrombocytopenic events. This system, however, could lead to inaccuracies and inefficiencies, causing alarm fatigue and compromised patient safety.