Development and evaluation of human-friendly explanations for sepsis early-warning models

败血症预警模型的人性化解释的开发和评估

• 批准号: 10546200
• 负责人: Wei-Jien Tan
• 金额: $ 25.43万
• 依托单位: SEPSIS SCOUT, INC.
• 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 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546200
• 关键词: Accident and Emergency department; Address; Agreement; Algorithm Design; Algorithms; American; Architecture; Artificial Intelligence; Attention; Autoimmune Diseases; Beneficence; Bioethics Consultants; Caring; Chemotherapy-Oncologic Procedure; Clinical; Critical Care; Data; Data Set; Dependence; Development; Diagnosis; Diagnostic; Ethics; Evaluation; Expert Opinion; Goals; Health; Home; Hospitals; Hour; Human; Immunocompromised Host; Individual; Lead; Longitudinal Studies; Maps; Measures; Methods; Modeling; Monitor; Nonmaleficence; Oncology; Organ Transplantation; Outpatients; Output; Patient Care; Patient Monitoring; Patient Representative; Patients; Performance; Phase; Physicians; Publishing; Risk; Sampling; Scoring Method; Sepsis; Sepsis Syndrome; Technology; Testing; Time; Transplantation Surgery; Validation; Visualization; artificial intelligence algorithm; base; clinical decision-making; deep learning algorithm; disorder risk; graphical user interface; high risk; human data; improved; innovation; machine learning algorithm; monitoring device; mortality; neural network; patient population; prediction algorithm; prevent; programs; public database; risk prediction; septic patients; success; time use; wearable device

Project Summary The goal of this Phase I project is to render a proprietary artificial intelligence algorithm for sepsis prediction developed by Patchd, Inc., compatible with explainability capabilities, providing clinicians underlying reasons for increased disease risk, as opposed to a “black box” risk prediction alone. The algorithm is additionally innovative in that it imputes sepsis risk based on vital sign data gathered from wearable devices, providing constant real-time surveillance of high-risk patients at home without requiring in-patient monitoring. Sepsis has a high mortality rate of approximately 40% and often occurs in immunocompromised patients such as those undergoing cancer chemotherapy, organ transplant surgery, or treatment for autoimmune conditions. Catching the condition early remains the best way to reduce mortality as well as prevent the long-term health effects associated with post-sepsis syndrome, which occurs in up to 50% of sepsis patients. Artificial intelligence (AI) represents an intriguing means of identifying sepsis early. By compiling vital sign data from high-risk patients and analyzing any changes in real-time, machine learning algorithms have shown promise in identifying at-risk patients up to 8 hours before sepsis onset in hospital settings. While these predictive algorithms demonstrate the power of AI in patient monitoring, most high-risk patients are treated via out-patient care without constant vital sign monitoring. Patchd, Inc. has developed an AI algorithm for sepsis prediction which employs vital sign data generated using wearable devices, such as watches or patches which record patient data in real-time. Thus far, the algorithm has been shown to improve sepsis prediction accuracy and provide earlier warnings for sepsis onset compared to other scoring methods. Recently however, bioethicists and regulators have called for AI algorithms to pair their predictions with supporting explanatory information providing both clinicians and patients more transparency when considering treatment options. During this Phase I program, Patchd will employ its current algorithm architecture and add so-called explainability functions, revealing to the user which specific vital sign dynamics impacted a given prediction. Vital sign data will be analyzed for local explanation functionality using both kernel and deep SHapley Additive exPlanations (SHAP). Data will also be analyzed to evaluate the significance of data points over time, using attentional mechanisms to understand the importance of vital sign changes over time. Primary human data sets, comprising true-positives, true-negatives, false-positives, and false-negatives, will be tested using the algorithm to evaluate both its predictive and explainabilty capabilities. Given that sepsis impacts 1.7 million Americans each year, the Patchd approach to explainable sepsis prediction will address a large and growing market opportunity.

项目摘要 这个第一阶段项目的目标是为脓毒症呈现一种专有的人工智能算法 Patchd，Inc.开发的预测与可解释性功能兼容，为临床医生提供潜在的 疾病风险增加的原因，而不是单纯的“黑箱”风险预测。该算法是 此外，它的创新之处在于，它基于从可穿戴设备收集的生命体征数据来计算脓毒症风险， 在家中提供对高危患者的持续实时监测，而无需住院监测。 脓毒症的死亡率很高，约为40%，通常发生在免疫功能低下的患者中，如 那些正在接受癌症化疗、器官移植手术或治疗自身免疫性疾病的人。 及早发现疾病仍然是降低死亡率和预防长期健康的最好方法。 与败血症后综合征相关的影响，该综合征发生在多达50%的脓毒症患者中。人工智能 (AI)代表了一种有趣的早期识别脓毒症的方法。通过收集高危患者的生命体征数据 通过实时分析任何变化，机器学习算法在识别风险方面显示出了希望 在医院环境中，患者在脓毒症发病前8小时内。虽然这些预测算法表明 人工智能在患者监控方面的力量，大多数高危患者都是通过门诊治疗，而不是一成不变的 生命体征监测。Patchd公司开发了一种用于脓毒症预测的人工智能算法，该算法使用生命体征 使用可穿戴设备生成的数据，例如实时记录患者数据的手表或贴片。因此， 到目前为止，该算法已被证明可以提高脓毒症的预测精度，并提供脓毒症的早期警告 与其他计分方法相比，起始值更高。然而，最近，生物伦理学家和监管机构呼吁人工智能 算法将他们的预测与支持解释信息配对，为临床医生和患者提供 在考虑治疗方案时更具透明度。在此第一阶段计划期间，Patchd将使用其 当前的算法架构并增加了所谓的可解释性功能，向用户揭示了哪些特定的关键 符号动力学影响了一个给定的预测。生命体征数据将针对本地解释功能进行分析 使用内核和深度Shapley加性解释(Shap)。还将分析数据以评估 随着时间推移数据点的重要性，使用注意机制来理解生命体征的重要性 随着时间的推移而改变。主要人类数据集，包括真阳性、真阴性、假阳性和 假阴性，将使用该算法进行测试，以评估其预测和可解释性能力。 鉴于脓毒症每年影响170万美国人，Patchd方法可以解释脓毒症的预测 将抓住一个巨大且不断增长的市场机遇。