CoAI: Cost-Aware Artificial Intelligence for Efficient Prehospital Diagnosis of Trauma Patients

项目总结/摘要 虽然人工智能（AI）和机器学习（ML）在整个医学领域得到了广泛的应用， 对ML模型预测成本的分析非常有限。例如，ML模型可以 准确预测创伤患者将患有急性创伤性凝血病（ATC），一种出血性疾病; 然而，它可能严重依赖于难以测量的患者特征，如血压或格拉斯哥昏迷评分， 这样做.标准ML技术并不优先考虑及时诊断，这是减少死亡和伤害的关键。 这个想法，我们称之为成本感知预测，是机器学习中最近感兴趣的话题。然而，在这方面， 现有的方法有很大的局限性，并且它们的临床效果还没有得到证实。这 提案将采用ML和可解释AI的最新进展，以1）开发改进的成本感知预测 技术. 2)使用临床数据证明其价值，3）将其整合到电子医疗中 记录这些方法将适用于科学和医学的许多领域。 目标1。开发一种新的基于特征重要性的成本感知预测方法。无现行办法 因为成本感知预测始终优于其他预测，并且每种预测都有自己的优点和缺点。 这项提案利用机器学习领域的最新发现，设计出一种新算法CoAI，具有新的优势 更少的弱点。CoAI将大幅提高预测性能，实现大规模分析 数据集，并灵活地使用任何ML模型。初步结果表明，CoAI可以超越现有的 方法.将创建一个新的成本感知预测公共基准，并用于将CoAI与 现有的方法，CoAI将作为易于使用的开源软件发布。 目标二。评估CoAI节省临床时间的潜力。CoAI预测出血性疾病的能力将 在一个前所未有的详细数据集上进行了测试，该数据集将创伤医院数据与医生调查相结合， 医护人员将CoAI与临床实践中使用的风险评分进行比较，将提供明确的估计， CoAI可以节省多少时间以及它可以防止多少误诊。初步分析显示 与现有的风险评分相比，CoAI减少了预测时间并提高了准确性。 目标3。将交互式ML方法纳入医疗记录。CoAI将被整合到 电子病历（EMR），使用专业护理人员的反馈。时间的定量估计 成本节约和主观印象将从护理人员和开放式采访中收集 将评估他们对CoAI等交互式机器学习方法的感受。这些见解 将指导未来交互式机器学习方法的研究，以及可能的临床研究工作。 CoAI对模拟创伤场景中决策的影响。 该项目的成功完成将有助于更快、更准确地诊断急性疾病， 推进机器学习和人工智能的最新发展。

Project Summary/Abstract While artificial intelligence (AI) and machine learning (ML) are becoming widely used throughout medicine, the analysis of the cost of an ML model’s predictions has been very limited. For example, an ML model may accurately predict that a trauma patient will have acute traumatic coagulopathy (ATC), a bleeding disorder; however, it may heavily rely on hard-to-measure patient features, like blood pressure or Glasgow Coma Score, to do so. Standard ML techniques do not prioritize timely diagnosis, which is key to minimize death and injury. This idea, which we refer to as cost-aware prediction, is a topic of recent interest in machine learning. However, existing methods have substantial limitations, and their clinical impact has not been demonstrated. This proposal will adopt recent advances in ML and explainable AI to 1) develop improved cost-aware prediction techniques. 2) demonstrate their value using clinical data and 3) integrate them into the electronic medical record. These methods will be applicable in many areas of science and medicine. Aim 1. Develop a novel feature importance-based approach for cost-aware prediction. No existing approach for cost-aware prediction consistently outperforms the others, and each has its own strengths and weaknesses. This proposal uses recent discoveries in machine learning to design a new algorithm, CoAI, with new strengths and fewer weaknesses. CoAI will substantially improve predictive performance, enable analysis on large datasets, and flexibly work with any ML model. Preliminary results show that CoAI can outperform existing methods. A new public benchmark for cost-aware prediction will be created and used to compare CoAI to existing methods, and CoAI will be published as easy-to-use open-source software. Aim 2. Evaluate CoAI’s potential for clinical time savings. CoAI’s ability to predict bleeding disorders will be tested on an unprecedentedly detailed dataset that combines trauma hospital data with surveys of doctors and paramedics. Comparing CoAI to the risk scores used in clinical practice will provide explicit estimates of how much time CoAI can save and how many misdiagnoses it can prevent. In preliminary analysis with trauma registry data, CoAI reduces prediction time and increases accuracy relative to an existing risk score. Aim 3. Incorporate an interactive ML method into the medical record. CoAI will be integrated into the electronic medical record (EMR), using feedback from professional paramedics. Quantitative estimates of time and cost savings and subjective impressions will be gathered from paramedics, and open-ended interviews will be conducted to assess their feelings about interactive machine learning methods like CoAI. These insights will guide future research in interactive machine learning methods, as well as possible clinical work to study CoAI’s impact on decision making in simulated trauma scenarios. Successful completion of this project will allow faster, more accurate diagnosis of acute illness and advance the state of the art in machine learning and artificial intelligence.