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Prediction of ICU Readmissions Using Data at Patient Discharge.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2018 Jul; Vol. 2018, pp. 4932-4935. - Publication Year :
- 2018
-
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.
Details
- Language :
- English
- ISSN :
- 2694-0604
- Volume :
- 2018
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 30441449
- Full Text :
- https://doi.org/10.1109/EMBC.2018.8513181