Unplanned ICU readmissions are defined as ICU patients who were rehospitalized within 30 days of hospital discharge.
Published in Chapter:
Machine Learning for Decision Support in the ICU
Yu-Wei Lin (Gies College of Business, University of Illinois at Urbana-Champaign, USA), Hsin-Lu Chang (National Chengchi University, Taiwan), Prasanna Karhade (University of Hawaiʻi at Mānoa, USA), and Michael J. Shaw (Gies College of Business, University of Illinois at Urbana-Champaign, USA)
Copyright: © 2023
|Pages: 16
DOI: 10.4018/978-1-7998-9220-5.ch090
Abstract
In this article, the authors provide an overview of the potential and challenges of machine learning for healthcare decision support. They first discuss the healthcare decision support ecosystems, including (1) beneficiaries, (2) health data, and (3) models. They then introduce the three main challenges of the healthcare decision support systems: data complexity, decision criticality, and model explainability. From there, they use unplanned intensive care unit readmission predictions in tackling the three main challenges of machine learning-based healthcare decision support systems. They investigate the data complexity issue by adopting dimension reduction techniques on patients' medical records to integrate patients' chart events, demographics, and the ICD-9 code. To address the decision criticality issue, they perform an in-depth deep learning performance analysis, and they analyze each feature's contribution to the predictive model. To unpack the model explainability issue, they illustrate the importance of each input feature and its combinations in the predictive model.