Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

中文翻译：

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利用物联网和机器学习进行重症监护病房的患者诊断和通气管理


未来的医疗保健系统将严重依赖临床决策支持系统（CDSS）来改善临床医生的决策过程。为了探索未来 CDSS 的设计，我们开发了一种以研究为重点的 CDSS，用于重症监护室患者的管理，该设备利用能够从呼吸机和其他医疗设备收集流式生理数据的物联网设备。然后，我们创建了机器学习模型，可以分析收集到的生理数据，以确定呼吸机是否正在提供潜在有害的治疗，以及是否存在致命的呼吸系统疾病，即急性呼吸窘迫综合征（ARDS）。我们还展示了将这些模型聚合到移动应用程序中的工作，该应用程序可以向提供者提供有关通气变化的响应式实时警报。正如最近的 COVID-19 大流行所表明的那样，能够准确预测新感染患者的 ARDS 有助于优先考虑护理。我们表明 CDSS 可用于分析临床事件识别和自动诊断的生理数据，并且我们还强调了医院 CDSS 的未来研究途径。