Subtyping complex phenotypes via constrastive learning by leveraging electronic health records

利用电子健康记录通过对比学习对复杂表型进行亚型分类

• 批准号: 10799083
• 负责人: Elior Rahmani
• 金额: $ 42.84万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10799083
• 关键词: Address; Asthma; Breast Cancer Patient; Characteristics; Classification; Clinical; Complex; Data; Databases; Dementia; Diabetes Mellitus; Diagnosis; Dimensions; Disease; Drug Interactions; Electronic Health Record; Equity; Estrogen Receptors; Ethnic Origin; Genetic; Genomics; Graves' Disease; Hashimoto Disease; Health Surveys; Healthcare; Heterogeneity; Hypertension; Hyperthyroidism; Hypothyroidism; Individual; Insulin; Learning; Location; Medical; Medicine; Mental disorders; Methods; Modeling; Obesity; Patients; Pattern; Phenotype; Population; Population Heterogeneity; Predisposition; Probability; Procedures; Process; Prognosis; Race; Reporting; Risk Factors; Source; Stratification; Structure; Surface; Techniques; Time; Training; Undifferentiated; Variant; autoimmune thyroid disease; cell type; clinical heterogeneity; clinical risk; clinically relevant; cohort; computer framework; disorder subtype; estrophilin; high dimensionality; improved; learning strategy; multidimensional data; neoplastic cell; novel; novel strategies; phenotypic data; portability; precision medicine; predictive modeling; public health relevance; racial population; response; success

Summary A critical step towards realizing the promise of precision medicine is the identification of biologically- and clinically-relevant disease subtypes. Disease subtypes are suspected yet unknown or not fully characterized for many conditions, including obesity, diabetes, hypertension, asthma, dementia, and psychiatric disorders. The existence of “phenotypic heterogeneity” has practical and clinical implications: undifferentiated cases of a disease may represent the action of a variety of underlying causal processes, each of which may have a different prognosis or respond to a different treatment. Existing phenotype subtyping methods predominantly rely on the idea that applying clustering or dimensionality reduction techniques to high-dimensional data from patients with a given condition may reveal explanatory patterns that correspond to disease subtypes. This implicitly assumes that biologically meaningful subtypes can be captured by the dominant axes of variation in the data. Yet, the most dominant sources of variation are expected to be independent of biologically meaningful subtypes in many settings. In this project, a novel contrastive learning method is proposed for learning a heterogeneity gradient of variation that is specific to cases of a given condition and cannot be found in matched controls. Electronic health records (EHR) and survey information from the rich All of Us database is expected to span the spectrum of clinical heterogeneity across common complex diseases, which can inform the proposed method about meaningful sub-phenotypic variation for many diseases. The subtypes identified will be evaluated within the All of Us database and replicated using three external EHR cohorts for subtype- specific genetic effects, clinical risk factors, and clinical trajectories. Finally, EHR-based models are notoriously known for their susceptibility to poor generalization on out-of-distribution data that represent locations, populations, medical practices, or other factors that were not represented in the training data. This challenge will be addressed by developing a domain generalization framework, which will allow learning disease subtypes that are generalizable across demographic characteristics, including location, ancestry, ethnicity, and race, which is essential to achieve equitable precision medicine and facilitate the integration of predictive models in healthcare pipelines.

概括 实现精确医学承诺的关键一步是鉴定生物学上和 临床上与疾病相关的亚型。怀疑疾病亚型但未知或未完全表征 对于许多情况，包括肥胖，糖尿病，高血压，哮喘，痴呆和精神疾病。 “表型异质性”的存在具有实际和临床意义：未分化的病例 疾病可能代表各种基本因果过程的作用，每种过程可能都有 不同的预后或对不同治疗的反应。现有的表型亚型方法主要是 依靠这样的想法，即将群集或降低降低技术应用于高维数据 给定病情的患者可能揭示与疾病亚型相对应的剥夺模式。这 隐含地假设生物学上有意义的亚型可以通过主要的变化轴捕获 数据。然而，最主要的变异来源有望独立于生物学上 在许多设置中有意义的亚型。在这个项目中，提出了一种新颖的对比学习方法 学习特定于给定条件案例的异质性变异梯度，找不到 在匹配的控件中。电子健康记录（EHR）和来自我们所有数据库的富人的调查信息是 预计将跨越普通复杂疾病的临床异质性的范围，这可以告知 关于许多疾病的有意义的亚表型变异的建议方法。确定的子类型 将在我们所有的数据库中进行评估，并使用三个外部EHR队列进行子类型的复制 特定的遗传作用，临床危险因素和临床轨迹。最后，基于EHR的模型是众所周知的 以其对代表位置的分布数据的概括不佳的敏感性而闻名的 培训数据中未代表的人群，医疗实践或其他因素。这个挑战 将通过开发域泛化框架来解决，这将允许学习疾病 在人口特征上可以推广的亚型，包括位置，祖先，种族和 种族，这对于获得公平的精确医学和促进预测的整合至关重要 医疗管道中的模型。