Autonomous system supporting patient-specific transfer and discharge decisions

意义：在这个SBIR项目中，我们建议提高AutoTriage的实用性，AutoTriage是一种基于机器学习的 临床决策支持（CDS）系统，通过将临床医生干预医疗信息整合到其 预测。尽管在患者转移和出院决策中确定了对CDS系统的需求， 工具不符合灵敏度和特异性的高标准。这是因为目前的CDS方法是 无法区分由于临床医生干预引起的患者健康变化与由于内部 自我平衡机制因此，例如，现有工具可能错误地建议患者出院 目前正在接受维持生命的治疗研究问题：机器学习原理可以 用于创建一个分类器，该分类器包含临床干预的迹象，以通知转移和出院 决策支持，最终导致更高质量的预测？此外，这样的工具是否能够 在对不同的患者人群或数据质量要求很高的人群进行测试时， 更穷？之前的工作：我们已经开发了AutoTriage，这是一种基于机器学习的CDSS，用于12小时死亡率 预测.在公开的MIMIC-III回顾性数据集上，该系统达到了 受试者工作特征曲线（AUROC）为0.88，优于常用的分诊评分（上级） MEWS（AUROC = 0.75），SOFA（0.71）和SAPS-II（0.72）在同一数据集。具体目标：整合 将临床医生干预信息导入现有AutoTriage软件（Aim 1），并测试其稳健性 修改工具以适应患者人群和数据质量的变化（目标2）。方法：我们将创造黄金 临床医生干预期间的标准，使用临床医生笔记中的图表事件和关键词。然后我们 我将训练一个二元分类器来识别这些时期，并最终使用该分类器来修改AutoTriage 成绩.将对回顾性UC雷克斯和稀疏MIMIC III数据库进行稳健性研究。 如果75%的临床医生干预时间正确，则证明成功完成了目标1 分类，如果ROC曲线下测试集面积比其当前值提高5%，并且如果30天 对于在最后一小时内接受治疗的患者，再入院预测的准确率要高出10%。目标2将是 如果AutoTriage ROC面积性能在UC雷克斯和 稀疏MIMIC III集。未来的方向：根据建议的工作，AutoTriage系统将 部署在我们正在进行的临床实施的地点。在这项研究中，我们预计AutoTriage将 每年评估25，000名ICU患者的死亡风险，帮助临床医生更有效地分配干预措施 总计一千五百万美元

Significance: In this SBIR project, we propose to improve the utility of AutoTriage, a machine-learning based clinical decision support (CDS) system, by integrating clinician intervention medical information into its predictions. Despite identified needs for CDS systems in patient transfer and discharge decisions, existing tools do not meet high standards for sensitivity and specificity. This is because current CDS methods are unable to distinguish changes in patient health due to clinician intervention from those arising due to an internal homeostatic mechanism. Thus, for example, existing tools may erroneously suggest discharge for a patient currently undergoing a life-sustaining treatment. Research Question: Can machine learning principles be used to create a classifier which incorporates signs of clinical intervention to inform transfer and discharge decision support, ultimately leading to higher quality predictions? In addition, will such a tool be able to maintain its performance when tested on a different patient population or one for which the data quality is much poorer? Prior Work: We have developed AutoTriage, a machine learning-based CDSS for 12-hour mortality prediction. On the publicly available MIMIC-III retrospective data set, this system attains an area under the receiver operating characteristic curve (AUROC) of 0.88, which is superior to commonly used triage scores MEWS (AUROC = 0.75), SOFA (0.71), and SAPS-II (0.72) on the same data set. Specific Aims: To integrate clinician intervention information into existing AutoTriage software (Aim 1), and to test the robustness of this modified tool to changes in patient population and data quality (Aim 2). Methods: We will create gold standards for periods of clinician intervention, using chart events and keywords from clinician notes. Then, we will train a binary classifier for identifying these periods and, ultimately, use the classifier to modify AutoTriage scores. Robustness studies will be performed on the retrospective UC ReX and sparse MIMIC III databases. Successful completion of Aim 1will be demonstrated if 75% of all hours of clinician intervention are correctly classified, if the test-set area under the ROC curve improves by 5% over its current value, and if 30-day readmission predictions are 10% more accurate for patients treated within the last hour. Aim 2 will be completed if AutoTriage ROC area performance is within ± 0.10 of its original value for both UC ReX and sparse MIMIC III sets. Future Directions: Following the proposed work, the AutoTriage system will be deployed at the sites of our ongoing clinical implementations. During this study, we project that AutoTriage will assess mortality risk for 25,000 ICU patients per year, helping clinicians more effectively allocate interventions totaling $15 million.