Affiliations 

  • 1 Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • 2 Department of Electrical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • 3 Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, 43000 Sungai Long, Malaysia
  • 4 Changshu Institute of Technology, Changshu, 215500 Jiangsu China
Arab J Sci Eng, 2021 Aug 16.
PMID: 34422543 DOI: 10.1007/s13369-021-06040-5

Abstract

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.