期刊文章%@ 2561-326X %I JMIR出版物%V 6 %N 卡塔尔世界杯8强波胆分析7 %P城市人口健康观察站疾病因果途径分析和决策支持:潜在可解释人工智能模型%A Brakefield,Whitney S %A Ammar,Nariman %A shabannejad,Arash %+生物医学信息学中心，田纳西大学健康科学中心，医学院，儿科，50 N Dunlap Street, R492，田纳西州孟菲斯，38103,1 901 287 5863，ashabann@uthsc.edu %K健康监测系统%K可解释AI %K决策支持%K机器学习%K肥胖%K慢性疾病%K精确健康预防%K语义推理%D 2022 %7 20.7.2022 %9原始论文%J JMIR Form Res %G英文%X背景:许多研究人员致力于开发慢性健康监测系统，以协助公共卫生决策。创建的一些数字健康解决方案缺乏向人类用户解释其决定和行动的能力。目的:本研究试图(1)通过加入语义层来扩展我们现有的城市人口健康观测站(UPHO)系统;(2)内聚性地使用机器学习和语义/逻辑推理，以提供可衡量的证据，并检测导致不良健康结果的途径;(3)提供临床用例场景和设计案例研究，以确定与肥胖流行相关的健康的社会环境决定因素，(4)设计一个仪表板，使用所提供的场景演示UPHO在肥胖监测背景下的使用。方法:系统设计包括一个知识图生成组件，提供来自相关感兴趣领域的上下文知识。该系统利用现有本体中的概念、属性和公理来利用语义。此外，我们使用公开的美国疾病控制和预防中心500城市数据集进行多变量分析。 A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. Results: In this study, we present 2 clinical case scenarios and a proof-of-concept prototype design of a dashboard that provides warnings, recommendations, and explanations and demonstrates the use of UPHO in the context of obesity surveillance, treatment, and prevention. While exploring the case scenarios using a support vector regression machine learning model, we found that poverty, lack of physical activity, education, and unemployment were the most important predictive variables that contribute to obesity in Memphis, TN. Conclusions: The application of UPHO could help reduce health disparities and improve urban population health. The expanded UPHO feature incorporates an additional level of interpretable knowledge to enhance physicians, researchers, and health officials' informed decision-making at both patient and community levels. International Registered Report Identifier (IRRID): RR2-10.2196/28269 %M 35857363 %R 10.2196/36055 %U https://formative.www.mybigtv.com/2022/7/e36055 %U https://doi.org/10.2196/36055 %U http://www.ncbi.nlm.nih.gov/pubmed/35857363