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

项目摘要 相当大比例的精神科住院患者在出院后30天内再次入院。重新入院注意事项 不仅具有破坏性，而且还给患者和家庭带来巨大的经济负担，是 不断上涨的医疗成本。因此，减少和预测计划外重新接纳是未得到满足的主要需求。 精神科护理。开发基于机器学习的自然语言处理预测工具 使用电子健康记录(EHR)是一个关键的优先事项，因为这种工具不仅可以用来帮助 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- 护理费用。建立有效的风险预测模型的一个关键方面是对时间结构的建模 叙事。关于历史和当前健康状态以及事件的时间安排的信息(例如，物质使用 开始/停止时间、最近自杀或症状的波动)，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，并将其固定在时间线上) 是训练ML分类器的关键一步。没有精神病学特定的资源或指南可用于建模 临床文本中的时间性，因此没有结合强健、可扩展和可解释的ML预测模型 时间信息已经被开发出来。 我们建议提供一个精神病学特定的时间关系标注方案，为以下内容构建开源工具 提取时间信息，并开发精神病患者再入院预测模型。目标1是一个 创建数据资源的目标是创建一个大型精神病学文本存储库，用于建立我们的再入院 分类器，取消识别数据子集以允许与研究社区共享，并创建一个图层 该子集的时态注释。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天后再入院的风险。在目标3中，我们构建并评估了多个版本的30天再入院风险分类器， 并将性能反馈给目标2，以改进时态建模。我们在TOP上开发了无监督聚类 我们的分类器来发现患者亚群。我们包括实际评估，包括与 人类专家和对模拟未来数据的模型性能的评估。这项研究汇集了一项 在精神病学表型和EHR应用方面经验丰富的团队，以及积极开发切割- 自然语言数据的ML EDGE方法。这项工作将作为未来翻译的基础 研究，包括将再入院分类器应用于临床工作流程和干预措施的临床试验 以降低再次入院的风险。

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.