Surgery Duration Prediction using Multitask Feature Selection

Efficient optimization of operation room (OR) activity poses a significant challenge for hospital managers due to the complex and risky nature of the environment. The traditional "one size fits all" approach to OR scheduling is no longer practical, and personalized medicine is required to meet the diverse needs of patients, care providers, medical procedures, and system constraints within limited resources. This paper aims to introduce a scientific and practical tool for predicting surgery durations and improving OR performance for maximum benefit to patients and the hospital. Previous works used machine-learning models for surgery duration prediction based on preoperative data. The models consider covariates known to the medical staff at the time of scheduling the surgery. However, model selection becomes crucial, where the number of covariates used for prediction depend on the available sample size. Our proposed approach utilizes multitask regression to select a common subset of predicting covariates for all tasks with the same sample size while allowing the model's coefficients to vary between them. A regression task can refer to a single surgeon or operation type or the interaction between them. By considering these diverse factors, our method provides an overall more accurate estimation of the surgery durations, and the selected covariates that enter the model may help to identify the resources required for a specific surgery. We found that when the regression tasks were surgeon-based or based on the pair of operation type and surgeon, our suggested approach outperformed the compared baseline suggested in a previous study. However, our approach failed to reach the baseline for an operation type-based task. By accurately estimating surgery durations, hospital managers can provide care to a greater number of patients, optimize resource allocation and utilization, and reduce waste. This research contributes to the advancement of personalized medicine and provides a valuable tool for improving operational efficiency in the dynamic world of medicine.

Medienart:	E-Artikel

Erscheinungsjahr:	2024
Erschienen:	2024

Enthalten in:	Zur Gesamtaufnahme - volume:PP
Enthalten in:	IEEE journal of biomedical and health informatics - PP(2024) vom: 08. März

Sprache:	Englisch

Beteiligte Personen:	Azriel, David [VerfasserIn] Rinott, Yosef [VerfasserIn] Tal, Orna [VerfasserIn] Abbou, Benyamine [VerfasserIn] Rappoport, Nadav [VerfasserIn]

Links:	Volltext

Themen:	Journal Article

Anmerkungen:	Date Revised 18.03.2024 published: Print-Electronic Citation Status Publisher

doi:	10.1109/JBHI.2024.3374783

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	NLM369470575