Exploratory Research Project - ADAPT

探索性研究项目 - ADAPT

基本信息

批准号：
10577122
负责人：
Feifan Liu
金额：
$ 10.48万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-05 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10577122
关键词：
Acceleration Address Adoption Age Algorithms Big Data Caring Clinic Clinical Complex Comprehensive Health Care Data Data Collection Data Scientist Data Set Data Sources Development Disparity population Effectiveness Electronic Health Record Environment Equity Ethnic Origin Evaluation Feedback Follow-Up Studies Foundations Funding Health Health Care Visit Health Promotion Health system Healthcare Healthcare Systems Individual Institution Insurance Coverage Intervention Knowledge Learning Machine Learning Manuals Maps Mental Health Modeling Mood Disorders National Institute of Mental Health Organizational Affiliation Patients Performance Prevention Prevention Research Primary Care Process Prognosis Race Research Research Personnel Research Project Grants Risk Risk Factors Socioeconomic Status Subgroup Suicide Suicide attempt Suicide prevention System Techniques Technology Testing Time Training Translating Translations United States Use Effectiveness Validation Visit Work advanced analytics age group clinical data repository clinical diagnosis clinical practice clinical predictors cohort data integration data modeling deep learning deep neural network design effective intervention ethnic minority evidence base health care delivery health care service health disparity heterogenous data high risk high risk population human-in-the-loop implementation facilitators improved informatics tool innovation interest learning progression learning strategy machine learning prediction algorithm marginalized population medical schools medical specialties patient population pilot test predictive modeling predictive tools racial minority risk prediction risk prediction model sex social disparities success suicidal risk usability

项目摘要

ADAPT (EXPLORATORY PROJECT): SUMMARY/ABSTRACT Significance: Machine learning-based risk algorithms have transformational potential to improve suicide risk identification. However, the lack of large-scale validations, transfer guidance, and automated learning-based adaptation impedes adoption in clinical practice. This project aims to address this translation gap by systematically assessing and improving a suicide risk algorithm’s generalizability and adaptability from an original development setting to a new healthcare system. Investigators: The transdisciplinary team has comprehensive expertise in applying advanced machine learning techniques on electronic health record (EHR) data for predictive modeling and prevention analytics (Liu, Aseltine, Simon), studying clinical diagnosis, prognosis and treatment of serious mood disorders and suicide (Rothschild), identifying and assessing suicide risk (Simon), and promoting health services delivery redesign through technology and implementing informatics tools in clinical settings (Gerber). Innovation: This pioneering study will comprehensively evaluate and improve the generalizability and adaptability of an evidence-based suicide risk algorithm in different contexts. The team will build a unified pipeline of Automated, Data-driven, AdaPtable, and Transferable learning for suicide risk prediction (ADAPT). The versatile ADAPT tool will be accessible to non-expert users and compatible with EHR common data model standards, providing a scalable, interpretable and sustainable solution to risk algorithm translation across different clinical contexts. Moreover, we will design an advanced deep learning approach for suicide risk prediction and evaluate its effectiveness on generalizability and adaptability. Approach: The proposed study aims to assess the generalizability of the Mental Health Research Network (MHRN) risk algorithm and explore transfer and ensemble learning to adapt a previously learned model from original data sources into a tailored one optimized for a new health system (Aim 1); develop a unified pipeline, ADAPT, to integrate data preprocessing, model assessment and adaptation, model interpretation, and automated learning; explore how ADAPT’s results can be used to help match individuals to a range of intervention approaches where specialized or intensive treatment is reserved for those with the highest risk (Aim 2); design an innovative deep learning approach and test its effectiveness using ADAPT (Aim 3a); engage stakeholders to better understand potential barriers and facilitators to implementation, iteratively improve ADAPT’s usability, acceptability, and feasibility through their feedback using validated scales (Aim 3b). Environment: The UMass Chan Medical School (UMass) has proven its ability to support this ambitious study by its success with numerous NIMH-funded systems-based suicide prevention studies. Impact: The study holds great potential for promoting the implementation of an evidence-based EHR suicide risk algorithm in clinical practice. Paired with effective interventions, it will enable improved suicide prevention.

适应（探索性项目）：总结/摘要意义：基于机器学习的风险算法具有降低自杀风险的变革潜力然而，缺乏大规模验证、转移指导和基于自动化学习的方法。适应阻碍了临床实践的采用该项目旨在通过以下方式解决这一翻译差距。系统地评估和改进自杀风险算法的普遍性和适应性新医疗保健系统的原始开发设置。调查人员：跨学科团队在应用先进机器方面拥有全面的专业知识用于预测建模和预防分析的电子健康记录 (EHR) 数据的学习技术（Liu， Aseltine, Simon），研究严重情绪障碍和自杀的临床诊断、预后和治疗（罗斯柴尔德），识别和评估自杀风险（西蒙），以及促进卫生服务提供重新设计通过技术和在临床环境中实施信息学工具（格柏）。创新：这项开创性的研究将全面评估和提高普遍性和基于证据的自杀风险算法在不同情况下的适应性该团队将建立一个统一的管道。用于自杀风险预测的自动化、数据驱动、AdaPtable 和可迁移学习 (ADAPT)。多功能 ADAPT 工具可供非专家用户使用，并与 EHR 通用数据模型兼容标准，为风险算法翻译提供可扩展、可解释和可持续的解决方案此外，我们将设计一种针对自杀风险的先进深度学习方法。预测并评估其普遍性和适应性的有效性。方法：拟议的研究旨在评估心理健康研究网络的普遍性 (MHRN) 风险算法并探索迁移和集成学习以适应以前学习的模型将原始数据源转化为针对新卫生系统优化的定制数据源（目标 1）； ADAPT，集成数据预处理、模型评估和适应、模型解释和自动化学习；探索如何使用 ADAPT 的结果来帮助将个人与一系列的人进行匹配为风险最高的人保留专门或强化治疗的干预方法（目标 2）；设计一种创新的深度学习方法并使用 ADAPT 测试其有效性（目标 3a）；利益相关者更好地了解实施的潜在障碍和促进因素，迭代改进通过使用经过验证的量表进行反馈，ADAPT 的可用性、可接受性和可行性（目标 3b）。环境：麻省大学陈医学院 (UMass) 已证明其有能力支持这一雄心勃勃的项目 NIMH 资助的众多基于系统的自杀预防研究取得了成功。影响：该研究对于促进基于证据的 EHR 自杀的实施具有巨大潜力临床实践中的风险算法与有效的干预措施相结合，将有助于改善自杀预防。