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运用机器学习预测术后患者报告结局

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.

机构信息

Department of Plastic and Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA.

出版信息

Am Surg. 2023 Jan;89(1):31-35. doi: 10.1177/00031348221109478. Epub 2022 Jun 18.

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.

摘要

患者报告的结局 (PROs) 使提供者能够识别治疗效果、术后恢复、生活质量和患者满意度方面的差异。通过允许从疾病特异性因素转变为患者视角,PROs 提供了一种针对特定患者的共同决策方法。人工智能 (AI) 和机器学习 (ML) 技术可以通过准确预测 PROs 来促进这种共同决策并改善患者的预后。本文旨在通过概述常见的预测算法和建模技术,以及当前在外科领域的应用和局限性,全面回顾 AI 和 ML 模型在预测手术后 PROs 方面的应用。

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