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 很大一部分住院精神病患者在出院后30天内再次入院。重新入院 不仅是破坏性的，而且还会给患者和家庭造成巨大的经济负担， 医疗费用的上涨。因此，减少和预测计划外再入院是 精神病治疗开发基于机器学习（ML）的自然语言处理（NLP）预测工具 使用电子健康记录（EHR）是一个关键的优先事项，因为这些工具不仅可以用来帮助目标， 向风险最大的患者提供资源密集型干预措施，但也会降低精神健康水平- 护理费用。建立有效的风险预测模型的一个关键方面是建模的时间结构， 叙述关于历史和当前健康状态以及事件时间的信息（例如，物质使用 开始/停止时间，自杀倾向或症状的近期波动），可能在预测再入院方面发挥关键作用 风险自然语言注释（即，标记文本，如事件、症状，并将其锚定在时间轴上） 是训练ML分类器的关键步骤。没有精神病学特定的资源或指导方针存在的建模 临床文本中的时间性，因此没有鲁棒的可扩展和可解释的ML预测模型， 已经开发了时间信息。 我们建议提供一个精神科特定的时间关系注释方案，建立开源工具， 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是一个 数据资源创建的目标是，我们创建一个大型的精神病学文本库，用于建立我们的再入院 分类器，对数据子集进行去标识化以允许与研究社区共享，并创建图层 的时间注释。在目标2中，我们从存储库中的数据中提取时间信息 创建时间图，并将图神经网络应用于这些图，以提取用于预测的特征 30-白天再入院风险。在目标3中，我们构建并评估了30天再入院风险分类器的多个版本， 并将性能反馈到目标2以改进时间建模。我们在上面开发了无监督聚类， 我们的分类器来发现患者亚组。我们包括实际的评估，包括比较， 人类专家和对模拟未来数据的模型性能评估。这项研究汇集了一个 一个在精神病表型和EHR应用方面经验丰富的团队，以及一个积极开发切割- ML中用于自然语言数据的边缘方法。这项工作将作为未来翻译的基础 研究，包括在临床工作流程和干预措施的临床试验中实施再入院分类器 降低再入院风险