Exploratory Research Project - ADAPT

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提高自杀风险的转型潜力 身份证明。然而，缺乏大规模的验证、转移指导和基于自动化的学习 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 系统评估和改进自杀风险算法的泛化能力和适应性 一个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， 研究严重心境障碍和自杀的临床诊断、预后和治疗 (Rothschild)，识别和评估自杀风险(Simon)，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：这项开创性研究将全面评估和提高普适性和 基于证据的自杀风险算法在不同背景下的适应性。该团队将建造一条统一的管道 用于自杀风险预测的自动化、数据驱动、适应性和可转移学习(Adapt)。这个 非专家用户可以使用通用的适配器工具，并与电子病历通用数据模型兼容 标准，为风险算法转换提供可扩展、可解释和可持续的解决方案 不同的临床情况。此外，我们将设计一种先进的深度学习方法来应对自杀风险 对其泛化能力和适应性进行预测和评价。 方法：拟议的研究旨在评估心理健康研究网络的概括性 (MHRN)风险算法和探索转移和集成学习以适应先前从 将原始数据来源转变为为新的保健系统量身定做的数据来源(目标1)；建立统一的渠道， 适应，将数据预处理、模型评估和适应、模型解释和 自动学习；探索Adapt的结果如何用于帮助将个人与一系列 为高危人群保留专门或强化治疗的干预方法 (目标2)；设计创新的深度学习方法，并利用适应(目标3a)测试其有效性；参与 利益相关者更好地了解实施的潜在障碍和促进者，反复改进 使用经过验证的量表(目标3b)通过反馈调整适应的可用性、可接受性和可行性。 环境：马萨诸塞州大学陈冯富珍医学院已经证明了它有能力支持这一雄心勃勃的计划 这项研究成功地完成了许多由NIMH资助的基于系统的自杀预防研究。 影响：该研究对促进循证EHR自杀的实施具有很大潜力 风险算法在临床实践中的应用。与有效的干预措施相结合，它将能够改进自杀预防。

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.