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）：摘要/摘要意义：基于机器学习的风险算法具有改善自杀风险的变革潜力鉴别。但是，缺乏大规模验证，转移指导和自动化的学习适应阻碍了临床实践中的采用。该项目旨在解决此翻译差距系统地评估和改善自杀风险算法的普遍性和适应性新的医疗保健系统的原始开发环境。调查人员：跨学科团队在应用高级机器方面具有全面的专业知识电子健康记录的学习技术（EHR）数据用于预测建模和预防分析（LIU， Aseltine，Simon），研究临床诊断，预后和严重情绪障碍和自杀的治疗（Rothschild），识别和评估自杀风险（Simon），并促进健康服务进行重新设计通过技术并在临床环境（Gerber）中实施信息工具。创新：这项开创性研究将全面评估和提高普遍性和在不同情况下，基于证据的自杀风险算法的适应性。团队将建立统一的管道自杀风险预测（Adapt）的自动，数据驱动，适应性和可转移的学习。非专家用户可以访问多功能适应工具，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不同的临床环境。此外，我们将设计一种自杀风险的先进深度学习方法预测并评估其对概括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）风险算法并探索转移和集合学习，以适应以前学习的模型原始数据源成针对新的卫生系统进行了优化的量身定制的数据源（AIM 1）；开发统一管道，适应，整合数据预处理，模型评估和适应性，模型解释以及自动学习；探索如何使用适应结果来帮助个人匹配一系列干预方法是为具有最高风险的人保留专业或强化治疗的方法（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（AIM 3A）；从事利益相关者更好地了解潜在的障碍和促进者实施，迭代地改善使用经过验证的量表（AIM 3B）通过反馈来调整其可用性，可接受性和可行性。环境：UMass Chan医学院（UMass）证明了其支持这一雄心勃勃的能力通过其成功进行了许多NIMH资助的基于系统的自杀预防研究的研究。影响：该研究具有促进基于证据的EHR自杀的巨大潜力临床实践中的风险算法。再加上有效的干预措施，它将可以改善自杀预防。