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

项目摘要 这个第一阶段项目的目标是为败血症提供一种专有的人工智能算法 由Patchd公司开发的预测，与解释能力兼容，为临床医生提供基础 这是一个简单的概念，它解释了疾病风险增加的原因，而不是仅仅是“黑匣子”风险预测。该算法 另外的创新在于其基于从可穿戴设备收集的生命体征数据来估算败血症风险， 在家中对高危患者进行持续的实时监测，而不需要住院患者监测。 脓毒症具有约40%的高死亡率，并且经常发生在免疫功能低下的患者中， 如接受癌症化疗、器官移植手术或自身免疫性疾病治疗的患者。 及早发现这种疾病仍然是降低死亡率和预防长期健康的最佳方法。 与脓毒症后综合征相关的影响，发生在高达50%的脓毒症患者中。人工智能 (AI)代表了一种早期发现败血症的有趣方法。通过收集高危患者的生命体征数据 并实时分析任何变化，机器学习算法在识别风险方面表现出了希望 患者在医院环境中脓毒症发作前8小时。虽然这些预测算法表明 人工智能在患者监测中的作用，大多数高风险患者通过门诊护理进行治疗， 生命体征监测Patchd，Inc.开发了一种用于脓毒症预测的AI算法， 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预测的准确性，并为脓毒症提供早期预警 与其他评分方法相比。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性信息配对，为临床医生和患者提供 在考虑治疗方案时更加透明。在第一阶段计划中，Patchd将使用其 当前的算法架构，并添加所谓的可解释性功能，向用户揭示哪些特定的重要 符号动态影响给定的预测。将分析生命体征数据，以实现本地解释功能 使用内核和深度SHapley加法解释（SHAP）。还将对数据进行分析，以评估 数据点随时间推移的意义，使用注意力机制了解生命体征的重要性 随着时间的推移而改变。原始人类数据集，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，以评估其预测和解释能力。 鉴于脓毒症每年影响170万美国人，Patchd方法可以解释脓毒症预测 将抓住一个巨大且不断增长的市场机会。

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.