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：
10591784
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/10591784
关键词：
Address Age Antipsychotic Agents Anxiety Characteristics 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 Randomized Clinical Trials Records Regimen Relapse Research Research Design Research Training Sampling Schizophrenia Scoring Method Services Standardization Symptoms Techniques Testing Time To specify Training Translating Treatment Effectiveness Treatment Protocols adjudicate adverse outcome affective disturbance associated symptom base 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 machine learning algorithm machine learning method network informatics novel outcome prediction patient oriented person centered personalized medicine predict clinical outcome psychiatric emergency psychosocial psychotic 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，情绪障碍有关，认知，合并症，重大社会和功能障碍的缺陷，是主要原因美国和全球的残疾。尽管抗精神病药和社会心理治疗是对于精神分裂症的某些符号有效，未建立所有符号的有效方案。这治疗指南的主要局限性是依赖RCT，该RCT测试有限的治疗及其对很少有符号和合并症。为解决障碍的各个方面的治疗试验的试验是过于复杂的复杂。数据驱动机器学习（ML）可以使用大观测来解决此差距数据集包含有关现实世界实践中使用的复杂有效方案的信息。 ML可以群集具有共同特征并确定针对其精神科和的独特方案的人临床合并症。这些新的治疗方案是可能的精确治疗方法。 ML算法可以然后预测这些不同簇（或类）给予的关键以患者为中心的结果治疗方案。检查这些治疗方案的比较有效性，以预测关键成果是下一步。带有观察数据的独特药物电子学方法可以模拟临床试验。积分分数方法解决了混杂的，模仿了实现的平衡 RCT中的随机化。这些工具将确定哪种精度治疗方案是最有效的对于这些数据集中的类。 ML发现的相关性取决于数据质量。主张最大，大多数国家代表反映现实世界社区实践模式的样本，但使用计费代码最初不是为研究设计的。电子健康记录（EHR）广泛，但由于偏见而受到限制由于其细胞的细节水平而导致的准确性和复杂性不完整。这个建议将通过对示例进行ML分析来建立这些数据集类型的优势和局限性数据集，医疗补助分析提取（MAX）国家样本以及观察性健康数据科学和 Informatics（OHDSI）网络纽约总理医院（INSP）EHR。该项目的增强将将更传统的多元和回归技术与ML发现进行比较，以确定ML是否是否提供其他信息。为了解决“研究实践”差距，ML结果将转化为个性化治疗规则，以为精神分裂症治疗提供临床实践。训练后研究目标1将在培训目标A和B中进行无监督和监督的学习目标1识别课程及其数据集中的管理治疗和研究目标2将预测这些治疗的结果：是时候到达了急诊科访问，重新入学和合并症事件的时间。研究目标3将使用在培训目标C中学到的药物ePIDEMIologic方法，以比较治疗的有效性，支持在此K-award结束时提交的R01，以测试国际EHR数据集中的有效性。