Applied Sciences, Год журнала: 2024, Номер 15(1), С. 296 - 296

Опубликована: Дек. 31, 2024

The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding how these models are divided analyzed, leaving gaps in normalization benchmarking. present research usually overlooks holistic for comparing ML-enabled ISs, significantly considering pivotal function criteria like precision, sensitivity, specificity. To address gaps, we conducted broad exploration 306 state-of-the-art papers to novel taxonomy IS management. We categorized studies six key areas, namely systems, treatment-planning monitoring resource allocation preventive hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is most effective parameter throughout all models. In addition, majority were published 2022 2023, with MDPI as leading publisher Python prevalent programming language. This extensive synthesis not only bridges but also proposes actionable insights ML-powered

Язык: Английский