López Beatriz, Raya Oscar, Baykova Evgenia, Saez Marc, Rigau David, Cunill Ruth, Mayoral Sacramento, Carrion Carme, Serrano Domènec, Castells Xavier
Control Engineering and Intelligent Systems (eXiT), University of Girona, Spain.
Institute of Health Care (ICS-IAS), Girona, Spain.
Heliyon. 2023 Jan 24;9(2):e13074. doi: 10.1016/j.heliyon.2023.e13074. eCollection 2023 Feb.
Clinical practice guidelines (CPGs) have become fundamental tools for evidence-based medicine (EBM). However, CPG suffer from several limitations, including obsolescence, lack of applicability to many patients, and limited patient participation. This paper presents APPRAISE-RS, which is a methodology that we developed to overcome these limitations by automating, extending, and iterating the methodology that is most commonly used for building CPGs: the GRADE methodology.
APPRAISE-RS relies on updated information from clinical studies and adapts and automates the GRADE methodology to generate treatment recommendations. APPRAISE-RS provides personalized recommendations because they are based on the patient's individual characteristics. Moreover, both patients and clinicians express their personal preferences for treatment outcomes which are considered when making the recommendation (participatory). Rule-based system approaches are used to manage heuristic knowledge.
APPRAISE-RS has been implemented for attention deficit hyperactivity disorder (ADHD) and tested experimentally on 28 simulated patients. The resulting recommender system (APPRAISE-RS/TDApp) shows a higher degree of treatment personalization and patient participation than CPGs, while recommending the most frequent interventions in the largest body of evidence in the literature (EBM). Moreover, a comparison of the results with four blinded psychiatrist prescriptions supports the validation of the proposal.
APPRAISE-RS is a valid methodology to build recommender systems that manage updated, personalized and participatory recommendations, which, in the case of ADHD includes at least one intervention that is identical or very similar to other drugs prescribed by psychiatrists.
临床实践指南（CPG）已成为循证医学（EBM）的基本工具。然而，CPG存在若干局限性，包括过时、对许多患者缺乏适用性以及患者参与度有限。本文介绍了APPRAISE-RS，这是一种我们开发的方法，通过对构建CPG最常用的方法——GRADE方法进行自动化、扩展和迭代，来克服这些局限性。
APPRAISE-RS依赖于临床研究的最新信息，并对GRADE方法进行调整和自动化，以生成治疗建议。APPRAISE-RS提供个性化建议，因为这些建议基于患者的个体特征。此外，患者和临床医生都表达了他们对治疗结果的个人偏好，在提出建议时会考虑这些偏好（参与式）。基于规则的系统方法用于管理启发式知识。
APPRAISE-RS已针对注意力缺陷多动障碍（ADHD）实施，并在28名模拟患者身上进行了实验测试。由此产生的推荐系统（APPRAISE-RS/TDApp）比CPG显示出更高程度的治疗个性化和患者参与度，同时在文献中最大的证据群体（EBM）中推荐最常见的干预措施。此外，将结果与四份盲法精神科医生处方进行比较，支持了该提议的有效性。
APPRAISE-RS是一种有效的方法，可用于构建管理更新、个性化和参与式建议的推荐系统，就ADHD而言，该系统包括至少一种与精神科医生开的其他药物相同或非常相似的干预措施。
