Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

Abstract

Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.

Artificial Intelligence Applications in Decision Making for Disease Management

“…"Prediction and risk strati cation" of disease was the second theme that was extracted from studies on AI applications in decision-making. Disease prediction and risk strati cation, forecast of disease occurrence, prediction of infectious diseases, prediction of aging-related diseases, prediction of disease risk for anomaly detection, prediction of mortality risk, prediction of future events, prediction of the spread of disease, and predictive models for diseases are some AI applications in disease prediction and their risk strati cation (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42). Therefore, AI has considerable capacity in disease prediction and risk strati cation that should be considered in para clinical centers.…”