Machine learning in the prediction of medical inpatient length of stay. Issue 2 (27th October 2021)

• Record Type: Journal Article
• Authors: Bacchi, Stephen; Tan, Yiran; Oakden‐Rayner, Luke; Jannes, Jim; Kleinig, Timothy; Koblar, Simon
• Is Part Of: Internal medicine journal. Volume 52:Issue 2(2022)
• Page Start: 176
• Page End: 185
• Publication Date: 2021-10-27
• Subjects: artificial intelligence -- deep learning -- neural network -- predictive analytics; Medicine -- Periodicals; 616
• DOI: 10.1111/imj.14962
• Languages: English
• ISSNs: 1444-0903

Abstract

Abstract: Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previous studies on the application of machine learning to the prediction of total hospital inpatient LOS for medical patients. A review of machine learning in the prediction of total hospital LOS for medical inpatients was conducted using the databases PubMed, EMBASE and Web of Science. Of the 673 publications returned by the initial search, 21 articles met inclusion criteria. Of these articles the most commonly represented medical specialty was cardiology. Studies were also identified that had specifically evaluated machine learning LOS prediction in patients with diabetes and tuberculosis. The performance of the machine learning models in the identified studies varied significantly depending on factors including differing input datasets and different LOS thresholds and outcome metrics. Common methodological shortcomings included a lack of reporting of patient demographics and lack of reporting of clinical details of included patients. The variable performance reported by the studies identified in this review supports the need for further research of the utility of machine learning in the prediction of total inpatient LOS in medical patients. Future studies should follow and report a more standardised methodology to better assess performance and to allow replication and validation. In particular, prospective validation studies and studies assessing the clinical impact of such machine learning models would be beneficial.