Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.

ABSTRACT:

Aim

In this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults.

Methods

We searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality.

Results

We screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both.

SUBMITTER: Askar M 

PROVIDER: S-EPMC11343463 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature