Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients

开发用于住院患者危重疾病识别、诊断和治疗的临床决策支持工具

• 批准号: 10683402
• 负责人: Matthew Michael Churpek
• 金额: $ 56.72万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683402
• 关键词: Acute Respiratory Distress Syndrome; Admission activity; Adult; Algorithms; Antibiotics; Caring; Cessation of life; Clinical; Complex; Congestive Heart Failure; Critical Care; Critical Illness; Data; Data Set; Decision Making; Deterioration; Diagnosis; Diagnostic; Early Diagnosis; Effectiveness; Electronic Health Record; Etiology; Event; Fatigue; Goals; Health; Health system; Hospital Mortality; Hospitalization; Hospitals; Hour; Human; Intensive Care; Laboratories; Lead; Learning; Life; Machine Learning; Manuals; Medical; Medical Errors; Mission; Modeling; Morbidity - disease rate; Natural Language Processing; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Prediction of Response to Therapy; Provider; Psychological reinforcement; Public Health; Recommendation; Research; Resources; Risk; Risk Factors; Sepsis; Source; Structure; Testing; Time; Trees; United States National Institutes of Health; Work; case-based; clinical decision support; clinical diagnosis; clinical practice; clinical risk; common treatment; convolutional neural network; cost outcomes; design; diagnostic accuracy; disability; discrete time; experience; graphical user interface; high risk; improved; improved outcome; innovation; iterative design; learning strategy; machine learning framework; machine learning method; machine learning model; mortality; multitask; novel; optimal treatments; personalized care; personalized intervention; predictive modeling; preventable death; recurrent neural network; satisfaction; simulation; structured data; support tools; tool; transfer learning; user centered design; ward

PROJECT SUMMARY Up to 5% of hospitalized adult patients on the medical-surgical wards develop clinical deterioration requiring intensive care. Medical errors are common before deterioration events, including delays and misjudgments in identification, diagnosis, and treatment, and these errors lead to increased morbidity and mortality. Therefore, it is critically important to improve the care of high-risk ward patients to decrease preventable in-hospital deaths. The current paradigm for attempting to decrease mortality from deterioration has several limitations. First, most early warning scores designed to identify high-risk patients are based only on vital signs and have limited accuracy. Clinical notes are an underutilized, rich source of information comprising nearly 80% of electronic health record (EHR) data. Natural language processing (NLP) can extract important risk factors from clinical notes for machine learning models to improve accuracy over existing tools. Second, current early warning scores only tell clinicians that a patient is at high risk but provide no information regarding what clinical condition is causing a patient’s deterioration. This leads to diagnostic and treatment errors, which results in worse patient outcomes. Developing tools to enhance diagnostic accuracy for high-risk ward patients could lead to fewer medical errors, decreased costs, and improved outcomes. Third, the initial treatment decisions for deteriorating patients are made by clinicians with limited experience caring for critically ill patients, which can result in delays of potentially life-saving therapies. By utilizing a large, granular, multicenter dataset, algorithms to predict the treatments a patient should receive can be developed, resulting in early, targeted, potentially life-saving therapy. The long-term goal is to develop and implement clinically useful decision support tools to decrease preventable death from deterioration. The overall objective of this project is to develop a clinical decision support tool for the identification, diagnosis, and treatment of patients at high risk of deterioration. This objective will be pursued in the following three specific aims: 1) Develop machine learning models to identify patients at high risk of deterioration using both structured data and unstructured clinical notes; 2) Develop models to predict the diagnosis that is causing the deterioration event and the potentially life-saving treatments that should be provided to high-risk patients; 3) Develop a clinical decision support tool with a graphical user interface incorporating the models from Aims 1 and 2 via user-centered design principles and then test its effectiveness, efficiency, and user satisfaction in a case-based simulation study. This research is innovative because it will utilize NLP, reinforcement learning, interpretable machine learning, and multi-task transfer learning approaches. The proposed research is significant because it will provide clinicians with powerful new tools that can be implemented in the EHR to identify, diagnose, and make treatment recommendations for high-risk patients. This will result in the delivery of early, personalized care to decrease preventable death from deterioration.

项目概要 内外科病房中多达 5% 的住院成年患者出现临床恶化，需要接受治疗 重症监护在病情恶化之前很常见，包括延误和误判。 识别、诊断和治疗，这些错误导致发病率和死亡率增加。 对于改善高危病房患者的护理以减少可预防的院内死亡至关重要。 目前试图降低恶化死亡率的模式有几个局限性。 大多数旨在识别高危患者的早期预警评分仅基于生命体征，并且作用有限 临床记录是一种未被充分利用的丰富信息源，占电子信息的近 80%。 健康记录（EHR）数据自然语言处理（NLP）可以从临床中提取重要的风险因素。 机器学习模型的注释，以提高现有工具的准确性。第二，当前的预警分数。 只告知患者处于高风险状态，但不提供有关临床状况的信息 导致患者病情恶化，这会导致诊断和治疗错误，从而导致患者病情恶化。 开发工具来提高高危病房患者的诊断准确性可能会减少结果。 第三，针对病情恶化的初步治疗决策。 患者是由护理危重患者经验有限的教区居民提供的，这可能会导致延误 通过利用大型、精细、多中心数据集和算法来预测可能挽救生命的疗法。 可以制定患者应接受的治疗方法，从而实现早期、有针对性、可能挽救生命的治疗。 长期目标是开发和实施临床上有用的决策支持工具，以减少 该项目的总体目标是开发临床决策支持。 该目标将是识别、诊断和治疗病情恶化高风险患者的工具。 追求以下三个具体目标：1）开发机器学习模型来识别高风险患者 使用结构化数据和非结构化临床记录来预测恶化情况；2) 开发模型来预测恶化情况； 导致恶化事件的诊断以及应提供的可能挽救生命的治疗 3）开发具有图形用户界面的临床决策支持工具，其中包含 通过以用户为中心的设计原则建立目标 1 和 2 的模型，然后测试其有效性、效率和 基于案例的模拟研究中的用户满意度这项研究是创新的，因为它将利用 NLP， 强化学习、可解释机器学习和多任务迁移学习方法。 拟议的研究意义重大，因为它将为上级提供强大的新工具，这些工具可以 在 EHR 中实施，以识别、诊断高危患者并提出治疗建议。 将导致提供早期、个性化的护理，以减少因病情恶化而导致的可预防的死亡。

项目成果

Development and external validation of deep learning clinical prediction models using variable-length time series data.

使用可变长度时间序列数据开发深度学习临床预测模型并进行外部验证。

• 发表时间: 2024-04-29
• 作者: Bashiri, Fereshteh S;Carey, Kyle A;Martin, Jennie;Koyner, Jay L;Edelson, Dana P;Gilbert, Emily R;Mayampurath, Anoop;Afshar, Majid;Churpek, Matthew M
• 通讯作者: Churpek, Matthew M

Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding.

用于构建一套临床自然语言处理任务的分层注释：进度注释理解。

• 发表时间: 2022-06
• 作者: Gao, Yanjun;Dligach, Dmitriy;Miller, Timothy;Tesch, Samuel;Laffin, Ryan;Churpek, Matthew M;Afshar, Majid
• 通讯作者: Afshar, Majid