Modeling Temporality with Natural Language Processing to Predict Readmission Risk of Patients with Psychosis

使用自然语言处理对时序进行建模以预测精神病患者的再入院风险

• 批准号: 10669207
• 负责人: Mei-Hua Hall
• 金额: $ 67.33万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10669207
• 关键词: Algorithms; Apache; Appearance; Clinical; Clinical Trials; Communities; Computational Linguistics; Data; Data Set; Databases; Economic Burden; Electronic Health Record; Evaluation; Event; Family; Feedback; Foundations; Future; Graph; Guidelines; Health; Health Care Costs; Healthcare; Hospitals; Human; Information Retrieval; Inpatients; Intake; Interpersonal Relations; Intervention; Intervention Trial; Knowledge; Learning; Machine Learning; Manuals; Measures; Medical; Mental Health Services; Mental disorders; Methods; Modeling; Moods; National Institute of Mental Health; Natural Language Processing; Occupations; Output; Patients; Performance; Phenotype; Play; Preventive measure; Process; Psychiatric therapeutic procedure; Psychiatry; Psychoses; Publications; Research; Resources; Risk; Risk Factors; Scheme; Standardization; Step training; Strategic Planning; Structure; Symptoms; System; Testing; Text; Time; Training; Update; Validation; Work; data resource; experience; graph neural network; hospital readmission; improved; large datasets; learning community; learning strategy; machine learning algorithm; machine learning classifier; machine learning prediction; model building; multiple datasets; natural language; open source; open source tool; patient subsets; personalized predictions; predictive modeling; predictive tools; readmission risk; repository; risk prediction; risk prediction model; structured data; substance use; suicidal; targeted delivery; timeline; tool; translational study

Project Summary A substantial proportion of psychiatric inpatients are readmitted within 30 days of discharge. Readmissions not only are disruptive but also cause enormous economic burden for patients and families, and are a key driver of rising healthcare costs. Reducing and predicting unplanned readmission are therefore major unmet needs of psychiatric care. Developing machine learning (ML)-based natural language processing (NLP) prediction tools using electronic health records (EHRs) is a key priority as such tools could not only be used to help target the delivery of resource-intensive interventions to those patients at greatest risk, but also reduce psychiatric health- care costs. A key aspect in building effective risk predictive models is the modeling of temporal structure in the narratives. Information about the historical and present health states and timing of events (e.g., substance use start/stop timing, recent fluctuations in suicidality or symptoms), may play a key role in predicting readmission risk. Natural language annotation (i.e., tagging text such as events, symptoms, and anchoring them on a timeline) is a key step for training ML classifiers. No psychiatry-specific resources or guidelines exist for the modeling of temporality in clinical text, and as a result no robust scalable and explainable ML predictive models incorporating temporal information have been developed. We propose to deliver a psychiatric specific temporal relation annotation scheme, build open-source tools for extracting temporal information, and develop readmission prediction models for psychiatric patients. Aim 1 is a data resource creation aim in which we create a large repository of psychiatric text for building our readmission classifier, de-identify a subset of that data to allow for sharing with the research community, and create a layer of temporal annotations for that subset. In Aim 2, we extract temporal information from the data in the repository to create temporal graphs, and apply graph neural networks to these graphs to extract features for predicting 30-day readmission risk. In Aim 3 we build and evaluate multiple versions of 30-day readmission risk classifiers, and feedback performance to Aim 2 to improve temporal modeling. We develop unsupervised clustering on top of our classifiers to discover patient sub-groups. We include practical evaluations including a comparison to human experts and an evaluation of model performance on simulated future data. The study brings together a team experienced in psychiatric phenotyping and application of EHRs, and a team active in developing cutting- edge methods in ML for natural language data. This work will serve as the foundation for future translational studies, including implementing readmission classifiers into clinical workflows and clinical trials of interventions to reduce readmission risk.

项目摘要 在出院后30天内，将大量的精神病院入院入院。再入院不是 仅是破坏性的，但也给患者和家庭造成巨大的经济负担，并且是 医疗保健费用上涨。因此，减少和预测计划外的再入院是主要的未满足需求 精神病护理。开发基于机器学习（ML）的自然语言处理（NLP）预测工具 使用电子健康记录（EHRS）是关键优先级，因为这种工具不仅可以用来帮助定位 向那些面临最大风险的患者提供资源密集型干预措施，但也可以减少精神病健康 - 护理费用。建立有效风险预测模型的关键方面是对时间结构的建模 叙述。有关历史和现在的健康状况和事件时机的信息（例如，物质使用 开始/停止时机，自杀或症状的最新波动）可能在预测再入院方面起关键作用 风险。自然语言注释（即，在时间表上标记文本，例如事件，症状并将其锚定） 是培训ML分类器的关键步骤。没有针对精神病学的资源或准则来建模 临床文本中的时间性，结果没有可靠和可解释的ML预测模型包含 时间信息已开发。 我们建议提供一种精神科特定的时间关系注释计划，为 提取时间信息，并为精神病患者开发再入院预测模型。目标1是 数据资源创建目标，我们创建了一个大型精神病学文本存储库来建立我们的再入院 分类器，否定数据的子集，以允许与研究社区共享，并创建一层 该子集的时间注释。在AIM 2中，我们从存储库中的数据中提取时间信息 创建时间图，并将图形神经网络应用于这些图形以提取用于预测的功能 30天再入院风险。在AIM 3中，我们构建和评估了30天再入院风险分类器的多个版本， 和反馈绩效，以改善时间建模。我们在顶部开发无监督的聚类 我们的分类器发现患者子组。我们包括实际评估，包括与 人类专家以及对模拟未来数据的模型性能的评估。该研究汇集了 EHR的精神病表型和应用的团队，以及积极发展进取的团队 自然语言数据中ML中的边缘方法。这项工作将成为未来翻译的基础 研究，包括将重新分类器实施到临床工作流程和干预的临床试验中 降低再入院风险。