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Predictive Analytics for the KMAP-O Model in Design and Evaluation of Diabetes Care Management Research

机译:糖尿病护理管理研究设计与评价KMAP-O模型的预测分析

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This is a commentary on methodological challenges and analytical requirements in designing an evaluation of the knowledge, motivation, attitude, preventive practice-outcome (KMAP-O) model for selfcare management of diabetes. Critical issues pertaining to an investigation of the dose-response relationship between the intervention program and outcomes, the comparative effectiveness evaluation, and the lengths of observation were noted. Although numerous publications on factors influencing diabetes care and control were systematically reviewed and documented in the literature, scientific results on artificial intelligence research remain to be uncovered. To optimizing the knowledge and clinical practice in selfcare management, specific methodological approaches to predictive analytics are suggested for future clinical studies, using a comprehensive behavioral system such as the KMAP-O model.
机译:这是对设计糖尿病自主管理的知识,动机,态度,预防实践 - 结果(KMAP-O)模型的评估来评论方法论挑战和分析要求。 注意到涉及干预计划和结果之间剂量 - 响应关系的关键问题,并注意到比较有效性评估和观察的长度。 虽然系统地审查并记录了许多关于影响糖尿病护理和对照的因素的出版物,但在文献中审查并记录了人工智能研究的科学结果仍未被发现。 为了优化自我管理中的知识和临床实践,建议使用KMAP-O模型等综合行为系统来提出对预测分析的具体方法论方法。

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