SCH: EXP: Collaborative Research: Group-Specific Learning to Personalize Evidence-Based Medicine

患者护理越来越多地受到基于证据的实践指南的指导，该指南被定义为“系统制定的声明，以帮助医生和患者针对特定的临床情况做出适当的医疗保健决策”。这些指南被视为改善患者护理和降低成本的关键，但目前的指南往往针对个体疾病，很少考虑患者病情的所有相关细节，例如年龄、性别和种族背景，以及患者患有的其他疾病。通过应对个性化循证医学的挑战，该项目的研究将对患有多种慢性病的患者产生积极影响，这正在成为美国人口老龄化的常态。 为此，该项目将开发新的临床建模技术，可以使用电子健康记录（EHR）中的可用数据来改进这些指南的个性化。更具体地说，该项目的最终目标是生成更加个性化的指南，这些指南可以在临床决策支持系统中实施，并由医生和其他人用于对多种慢性病患者进行综合治疗。为了应对处理多种慢性病的个性化护理指南的挑战，该项目开发了一个建模框架——群体特异性学习（GSL），能够通过使模型变得越来越个性化而不使模型过于具体来增强临床建模。特别是，GSL 建模范式应用于增强健康科学研究中常用的四种建模技术：生存分析、通过倾向评分进行因果分析、竞争风险模型和多状态模型。这项工作重点关注 II 型糖尿病 (T2DM)、其前兆、糖尿病前期、其合并症（高血压、肥胖、高脂血症）及其后果（慢性肾病、肾功能衰竭以及各种心脏和血管并发症）。糖尿病有许多相互关联的合并症和严重并发症，但治疗这些疾病的循证指南是单独治疗这些疾病的。为了解决这一限制，该项目开发了一套分析技术，可以考虑糖尿病人群中存在的巨大异质性，以衡量现有循证指南要素（干预措施）对糖尿病并发症进展风险的影响。然后可以将这些指南要素汇编成指南，从而可以对具有异质性的人群进行系统和综合的治疗。

Patient care is increasingly guided by evidence based practice guidelines, which are defined as "systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances." These guidelines are viewed as the key to improving patient care and reducing costs, but current guidelines tend to be specific to individual diseases, and rarely consider all of the relevant details of a patient's condition, such as age, gender, and ethnic background, as well as other diseases from which patients suffer. By addressing the challenge of personalized evidence based medicine, the research in this project will positively impact patients suffering from multiple chronic conditions, which is becoming the norm in the aging US population. To that end, this project will develop new clinical modeling techniques that can use the data available in electronic health records (EHRs) to improve the personalization of these guidelines. More specifically, the ultimate goal of this project is to generate more personalized guidelines that can be implemented in clinical decision support systems and used by physicians and others for the comprehensive treatment of patients with multiple chronic conditions.To address the challenge of personalized care guidelines to handle multiple chronic conditions, this project develops a modeling framework, Group-Specific Learning (GSL), with the ability to enhance clinical modeling by making models increasingly personalized without rendering them excessively specific. In particular, the GSL modeling paradigm is applied to enhance four modeling techniques commonly used in health sciences research: survival analysis, causal analysis via propensity scoring, competing risk models and multi-state models. This work focuses on type-II diabetes mellitus (T2DM), its precursor, pre-diabetes, its comorbidities (hypertension, obesity, hyperlipidemia), and its consequences (chronic kidney disease, renal failure and the various cardiac and vascular complications). Diabetes has a number of interrelated comorbidities and severe complications, but evidence-based guidelines for the treatment of these conditions treat these conditions in isolation. To address this limitation, this project develops a suite of analytics techniques that can take the substantial heterogeneity that exists in the diabetic population into account in order to measure the effect of existing evidence-based guideline elements (interventions) in terms of risk of progression to diabetic complications. These guideline elements can then be compiled into guidelines, thus allowing for the systemic and comprehensive treatment of the population with heterogeneity.