Exploratory Research Project - ADAPT

探索性研究项目 - ADAPT

• 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
• 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

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.

ADAPT（探索性项目）：总结/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 识别.然而，缺乏大规模的验证，转移指导和基于自动学习的 适应阻碍了临床实践中的采用。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通用性和适应性， 新的医疗保健系统的原始开发环境。 研究人员：跨学科团队在应用先进机器方面具有全面的专业知识 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），确定和评估自杀风险（西蒙），并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（Gerber）。 创新：这项开创性的研究将全面评估和提高普遍性， 基于证据的自杀风险算法在不同情况下的适应性。该团队将建立统一的管道 自动化，数据驱动，可适应和可转移的自杀风险预测学习（ADAPT）。的 非专家用户可以使用通用的ADAPT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， 不同的临床背景。此外，我们还将设计一种先进的深度学习方法， 预测和评价其推广性和适应性。 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，并探索转移和集成学习，以适应先前学习的模型， 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）;开发统一的管道， 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ADAPT的结果如何用于帮助将个人与一系列 干预方法，专门或强化治疗只针对风险最高的患者 (Aim 2）设计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， 通过使用经确认量表的反馈，评估ADAPT的可用性、可接受性和可行性（目标3b）。 环境：马萨诸塞大学陈医学院（UMass）已经证明了它有能力支持这一雄心勃勃的计划。 研究的成功与许多NIMH资助的基于系统的自杀预防研究。 影响：该研究具有很大的潜力，促进实施循证电子健康记录自杀 临床实践中的风险算法。配合有效的干预措施，它将有助于改善自杀预防。