Prediction of ICU Readmissions Using Data at Patient Discharge. Conference Paper uri icon

abstract

  • Unplanned readmissions to ICU contribute to high health care costs and poor patient outcomes. 6-7% of all ICU cases see a readmission within 72 hours. Machine learning models on electronic health record data can help identify these cases, providing more information about short and long-term risks to clinicians at the time of ICU discharge. While time-toevent techniques have been used in clinical care, models that identify risks over time using higher-dimensional, non-linear machine learning models need to be developed to present changes in risk with non-linear techniques. This work identifies risks of ICU readmissions at 24 hours, 72 hours, 7 days, 30 days, and bounceback readmissions in the same hospital admission with an AUROC for 72 hours of 0.76 and for bounceback of 0.84.

name of conference

  • 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

published proceedings

  • Annu Int Conf IEEE Eng Med Biol Soc

author list (cited authors)

  • Pakbin, A., Rafi, P., Hurley, N., Schulz, W., Harlan Krumholz, M., & Bobak Mortazavi, J.

citation count

  • 10

complete list of authors

  • Pakbin, Arash||Rafi, Parvez||Hurley, Nate||Schulz, Wade||Harlan Krumholz, M||Bobak Mortazavi, J

publication date

  • July 2018

publisher