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

患者护理越来越多地受到循证实践指南的指导，该指南被定义为“系统地制定声明，以帮助医生和患者就特定临床情况下的适当医疗保健做出决定。”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别疾病，很少考虑患者病情的所有相关细节，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，这在美国老龄化人口中正成为常态。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)中可用的数据来改进这些指南的个性化。更具体地说，这个项目的最终目标是生成更多的个性化指南，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综合治疗患有多种慢性病的患者。为了应对个性化护理指南处理多种慢性病的挑战，该项目开发了一个建模框架--特定于组的学习(GSL)，该框架能够通过使模型越来越个性化而不使它们过于具体来增强临床建模。特别是，GSL建模范式被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、通过倾向评分的因果分析、竞争风险模型和多状态模型。这项工作集中在II型糖尿病(T2 DM)，它的前驱-糖尿病，它的共病(高血压，肥胖症，高脂血症)，及其后果(慢性肾脏疾病，肾功能衰竭和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的并发症，但治疗这些疾病的循证指南是单独治疗这些疾病的。为了解决这一局限性，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实质性异质性，以便衡量现有循证指南要素(干预措施)在糖尿病并发症进展风险方面的效果。然后可以将这些准则要素汇编成准则，从而对具有异质性的人口进行系统和全面的治疗。

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.