Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system
Published in Scientific Reports, 2022
Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for early and timely interventions that can significantly improve patient outcomes. We utilize multimodal features derived from digital signal processing techniques and tensor formation, as well as the electronic health record (EHR), to create machine learning models that predict the occurrence of several life-threatening complications up to 4 hours prior to the event. In order to ensure that our models are generalizable across different surgical cohorts, we trained the models on a cardiac surgery cohort and tested them on vascular and non-cardiac acute surgery cohorts. The best performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0 …
Recommended citation: R. B. Kim, O. P. Alge, G. Liu, B. E. Biesterveld, G. Wakam, A. M. Williams, M. R. Mathis, K. Najarian and J. Gryak, "Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system", Sci Rep, vol. 12, no. 1, p. 11347, Dec. 2022, doi: 10.1038/s41598-022-15496-w. https://www.nature.com/articles/s41598-022-15496-w