Machine Learning for Precision Treatments in Schizophrenia

机器学习用于精神分裂症的精准治疗

基本信息

批准号：
10697385
负责人：
Natalie Bareis
金额：
$ 19.55万
依托单位：
NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-05 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10697385
关键词：
Address Age Antipsychotic Agents Anxiety Characteristics Classification Clinical Clinical Data Clinical Research Clinical Treatment Clinical Trials Code Cognition Combined Modality Therapy Community Practice Complex Consensus Data Data Science Data Set Databases Demographic Factors Diabetes Mellitus Diagnosis Diagnostic Diagnostic tests Dose Early treatment Effectiveness Electronic Health Record Emergency department visit Equilibrium Evidence based practice Goals Hospitals Impairment Incidence Individual Informatics International K-Series Research Career Programs Knowledge Laboratories Machine Learning Medicaid Medical Mental Depression Mental disorders Methods Modeling New York Outcome Patient-Focused Outcomes Patients Pattern Pharmaceutical Preparations Pharmacoepidemiology Population Precision therapeutics Presbyterian Church Procedures Psychiatry Randomized Records Regimen Relapse Research Research Design Sampling Schizophrenia Scoring Method Services Specific qualifier value Standardization Symptoms Techniques Testing Time Training Translating Treatment Effectiveness Treatment Protocols adjudication adverse outcome affective disturbance burden of illness clinical practice clinically relevant comorbidity comparative effectiveness comparative effectiveness study compare effectiveness data quality disability effective therapy effectiveness testing first episode psychosis functional disability health data hospital readmission improved individualized medicine information model innovation learning strategy machine learning algorithm machine learning method network informatics novel outcome prediction person centered personalized medicine predict clinical outcome psychiatric emergency psychosocial psychotic symptoms randomized, clinical trials reduce symptoms residence response sex social societal costs supervised learning tool treatment effect treatment guidelines unsupervised learning

项目摘要

Project Summary/Abstract Schizophrenia is associated with psychotic symptoms, mood disturbances, deficits in cognition, comorbidities, significant social and functional impairment and is a leading cause of disability in the U.S. and worldwide. Although antipsychotic medications and psychosocial treatments are effective for some symptoms of schizophrenia, effective regimens for all symptoms are not established. The primary limitation of treatment guidelines is reliance on RCTs that test limited treatments and their effects on few symptoms and comorbidities. Trials of treatments administered to address all aspects of impairment is prohibitively complex. Data driven machine learning (ML) can address this gap using large observational datasets with information about complex and effective regimens used in real-world practice. ML can cluster individuals with shared characteristics and identify unique regimens administered for their psychiatric and clinical comorbidities. These new treatment regimens are possible precision treatments. ML algorithms can then predict critical patient-centered outcomes for these different clusters (or classes) administered these treatment regimens. Examining the comparative effectiveness of these treatment regimens that predict critical outcomes is an essential next step. Unique pharmacoepidemiologic methods with observational data can simulate clinical trials. Propensity score methods address confounding, mimicking balance achieved by randomization in RCTs. These tools will determine which precision treatment regimens are the most effective for the classes in these datasets. Relevance of ML findings depends on data quality. Claims have the largest, most nationally representative samples reflecting real-world community practice patterns but use billing codes not originally designed for research. Electronic health records (EHR) are extensive but limited due to bias from incomplete records with uncertain accuracy and complexity due to their granular level of detail. This proposal will establish the strengths and limitations of these dataset types by conducting ML analyses on exemplar datasets, a Medicaid Analytic eXtract (MAX) national sample, and the Observational Health Data Sciences and Informatics (OHDSI) network New York-Presbyterian Hospital (iNYP) EHR. An enhancement to this project will compare more traditional multivariate and regression techniques to the ML findings identifying whether ML provides additional information. To address the “research-practice” gap the ML results will be translated into personalized treatment rules to inform clinical practice for schizophrenia treatment. After training in unsupervised and supervised learning in Training Aims A and B, Research Aim 1 will identify classes and their administered treatments in the datasets and Research Aim 2 will predict outcomes of those treatments: time to emergency department visit, time to re-admission and incidence of comorbidities. Research Aim 3 will use pharmacoepidemiologic methods learned in Training Aim C to compare effectiveness of the treatments, supporting an R01 submitted at the end of this K-award to test effectiveness in an international EHR dataset.

项目摘要/摘要精神分裂症与精神病症状、情绪障碍、认知缺陷、合并症、严重的社会和功能障碍，是导致尽管抗精神病药物和心理社会治疗在美国和世界各地都很常见。对精神分裂症的某些症状有效，但尚未建立针对所有症状的有效治疗方案。治疗指南的主要局限性是依赖随机对照试验来测试有限的治疗方法及其对患者的影响很少有针对症状和合并症的治疗试验来解决各个方面的损伤。数据驱动的机器学习 (ML) 可以通过大量观察来解决这一差距。包含有关现实世界实践中使用的复杂且有效的方案的信息的数据集可以进行聚类。具有共同特征的个体，并确定针对其精神和疾病的独特治疗方案这些新的治疗方案可以通过机器学习算法进行精确治疗。然后预测这些不同集群（或类别）管理的以患者为中心的关键结果检查这些治疗方案的比较有效性，以预测危重情况。结果是下一步重要的一步，采用独特的药物流行病学方法和观察数据。模拟临床试验的倾向评分方法解决了混杂问题，模仿了通过方法实现的平衡。随机对照试验中的随机化将确定哪些精准治疗方案是最有效的。对于这些数据集中的类别，机器学习结果的相关性取决于数据质量。大多数具有全国代表性的样本反映了现实世界的社区实践模式，但使用计费代码最初不是为研究而设计的电子健康记录（EHR）虽然广泛，但由于偏见而受到限制。由于其详细程度，记录不完整，准确性和复杂性不确定。将通过对示例进行机器学习分析来确定这些数据集类型的优点和局限性数据集、医疗补助分析提取 (MAX) 国家样本以及观察健康数据科学和信息学 (OHDSI) 网络纽约长老会医院 (iNYP) EHR 将对此项目进行增强。将更传统的多变量和回归技术与 ML 结果进行比较，以确定 ML 是否提供更多信息，以解决“研究与实践”之间的差距，机器学习结果将被转化为个性化治疗规则为精神分裂症治疗的临床实践提供信息。训练目标 A 和 B 中的无监督和监督学习，研究目标 1 将确定类别及其数据集中进行的治疗和研究目标 2 将预测这些治疗的结果：时间急诊科就诊、重新入院时间和合并症发生率将使用研究目标 3。在培训目标 C 中学习的药物流行病学方法用于比较治疗的有效性，支持在本 K 奖结束时提交的 R01，以测试国际 EHR 数据集的有效性。