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.

项目摘要 在内科-外科病房中，高达5%的住院成人患者出现临床恶化， 重症监护在恶化事件之前，医疗错误是常见的，包括延误和误判， 这些错误导致发病率和死亡率的增加。因此 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范例有几个局限性。第一、 大多数用于识别高危患者的早期预警评分仅基于生命体征， 精度临床笔记是一种未充分利用的丰富信息来源，包括近80%的电子 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。二、当前预警评分 仅告知临床医生患者处于高风险状态，但不提供有关临床状况的信息 导致病人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 结果。开发工具来提高高风险病房患者的诊断准确性可能会减少 医疗错误，降低成本，改善结果。第三，最初的治疗决定恶化 病人是由经验有限的临床医生照顾危重病人，这可能导致延误 有可能挽救生命的疗法通过利用大型、粒度、多中心数据集，预测 可以开发患者应该接受的治疗，从而产生早期的、有针对性的、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 可预防的恶化死亡。本项目的总体目标是开发一个临床决策支持系统， 用于识别、诊断和治疗高恶化风险患者的工具。这一目标将是 我们追求以下三个具体目标：1）开发机器学习模型，以识别高风险患者 使用结构化数据和非结构化临床记录的恶化; 2）开发模型来预测 导致恶化事件的诊断和应提供的潜在救生治疗 3）开发一个具有图形用户界面的临床决策支持工具， 通过以用户为中心的设计原则，从目标1和目标2的模型，然后测试其有效性，效率， 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。的 拟议的研究意义重大，因为它将为临床医生提供强大的新工具， 在EHR中实施，以识别，诊断和为高风险患者提供治疗建议。这 将导致早期提供个性化护理，以减少可预防的恶化死亡。

项目成果

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

• DOI: 10.48550/arxiv.2204.03035
• 发表时间: 2022-04
• 期刊: LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation
• 作者: Yanjun Gao;Dmitriy Dligach;Timothy Miller;S. Tesch;Ryan Laffin;M. Churpek;M. Afshar