Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning.

ABSTRACT:

Background

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.

Objectives

This study shows how machine learning (ML) models can provide auxiliary information for more accurate identification of critical thrombocytopenic patients when compared with the traditional notification system.

Research design

A total of 50,505 patients' platelet count and other 26 additional laboratory datasets of each thrombocytopenic event were used to build prediction models. Conventional logistic regression and ML methods, including random forest (RF), artificial neural network, stochastic gradient descent (SGD), naive Baye

SUBMITTER: Lien F 

PROVIDER: S-EPMC7993911 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature