Machine Learning in Healthcare: Transforming Patient Outcome Prediction and Decision Support

Abstract

This review integrates evidence from 24 pivotal studies investigating the application of machine learning (ML) towards predicting clinical outcomes across various healthcare environments. Literature is shown to indicate that ML methods are highly effective at improving the predictability of patient outcomes relative to conventional models. Applications span from emergency department triage and sepsis identification to psychiatric evaluation and postoperative recovery, integrating templated data like electronic health records, administrative claims, imaging, and patient-reported outcomes. Supervised learning models—most prominently gradient boosting, random forests, and neural networks—prevail in outcome prediction tasks, with increasing interest in causal ML methods for treatment effect estimation. Multiple studies emphasize ML's potential to provide timely and individualized insights, supporting early intervention and enhancing decision-making. Despite positive findings, interpretability, generalizability, data quality, and workflow integration still pose challenges. Rigorous validation, ethical handling of data, and explainable models are highlighted throughout the literature. Finally, ML has the power to transform predictive healthcare by facilitating timely, data-informed, and patient-oriented care delivery.

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