Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit

Gregory B. Rehm, Sang Hoon Woo, Xin Luigi Chen, Brooks T. Kuhn, Irene Cortes-Puch, Nicholas R. Anderson, Jason Y. Adams, Chen Nee Chuah

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
JournalIEEE Pervasive Computing
StateAccepted/In press - Jan 1 2020


  • Biomedical monitoring
  • Couplings
  • Feature extraction
  • Lung
  • Medical services
  • Monitoring
  • Training

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computational Theory and Mathematics


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