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

项目摘要/摘要 虽然人工智能(AI)和机器学习(ML)正在整个医学中广泛使用， 对ML模型预测成本的分析一直非常有限。例如，ML模型可以 准确预测创伤患者会有急性创伤性凝血障碍(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， 要做到这一点。标准的ML技术不优先考虑及时诊断，而及时诊断是将死亡和伤害降至最低的关键。 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一个话题。然而， 现有的方法有很大的局限性，它们的临床影响还没有得到证实。这 提案将采用ML和可解释人工智能的最新进展来1)开发更好的成本感知预测 技巧。2)利用临床数据论证它们的价值；3)将它们集成到电子医疗系统中 唱片。这些方法将适用于科学和医学的许多领域。 目的1.提出一种新的基于特征重要性的代价感知预测方法。没有现有的方法 因为有成本意识的预测总是好于其他预测，而且每种预测都有自己的优势和劣势。 该方案利用机器学习领域的最新发现，设计了一种具有新优势的新算法COAI 更少的弱点。COAI将极大地提高预测性能，支持对大型 数据集，并灵活地使用任何ML模型。初步结果表明，COAI的性能优于现有的 方法：研究方法。将创建一个新的成本感知预测的公共基准，并将其用于比较COAI与 现有的方法和COAI将作为易于使用的开源软件发布。 目的2.评估COAI在节省临床时间方面的潜力。COAI预测出血性疾病的能力将是 在史无前例的详细数据集上进行测试，该数据集将创伤医院数据与对医生和 医护人员。将COAI与临床实践中使用的风险分数进行比较，将提供有关如何 COAI可以节省多少时间，以及它可以防止多少误诊。对创伤的初步分析 与现有风险分值相比，COAI减少了预测时间并提高了准确性。 目的3.将交互式ML方法引入到病历中。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访谈中收集 将进行一项调查，以评估他们对COAI等交互式机器学习方法的感受。这些见解 将指导未来在交互式机器学习方法方面的研究，以及可能的临床工作研究 COAI在模拟创伤场景中对决策的影响。 该项目的成功完成将使人们能够更快、更准确地诊断急性疾病和 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.