Predictive modeling in urgent care: a comparative study of machine learning approaches.

ABSTRACT:

Objective

The growing availability of rich clinical data such as patients' electronic health records provide great opportunities to address a broad range of real-world questions in medicine. At the same time, artificial intelligence and machine learning (ML)-based approaches have shown great premise on extracting insights from those data and helping with various clinical problems. The goal of this study is to conduct a systematic comparative study of different ML algorithms for several predictive modeling problems in urgent care.

Design

We assess the performance of 4 benchmark prediction tasks (eg mortality and prediction, differential diagnostics, and disease marker discovery) using medical histories, physiological time-series, and demographics data from the Medical Informa

SUBMITTER: Tang F 

PROVIDER: S-EPMC6951928 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature