I-Corps: Postoperative Risk Prediction for Heart Failure Patients

Texas Tech University, Lubbock TX

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of a risk assessment platform embedded with novel machine learning algorithms to predict mortality and adverse events in heart failure patients after cardiac treatments and surgeries. The technology seeks to: 1) address uncertainty and data imbalance issues prevalent in healthcare data, 2) extract important features and biomarkers relevant to post-operative risks, and 3) develop new tools for ultrasound image analysis to predict the probability of adverse events. There is currently no established technology for healthcare professionals in the cardiac care field to analyze multiple data sources for risk assessment pre- and post-cardiac treatment. The proposed technology may help clinicians leverage advanced machine learning tools for treatment outcomes assessment and clinical decision-making. The technology may also promote the use of data science approaches and computer models in other clinical applications. The project seeks to enable accurate risk prediction and enhance confidence in post-operative treatment, improving the quality of cardiac care, and ultimately benefitting cardiac patients. This I-Corps project is based on the development of a software application to predict post-operative risk for end-stage heart failure patients after cardiac surgeries such as heart transplantation or left ventricular assist device (LVAD) implantation. The software application has a back-end server with novel machine learning algorithms trained using electronic health records (EHR) from previous heart transplant and LVAD recipients. Risk prediction is performed using customized machine learning algorithms and statistical risk models. Novel clustering techniques are used to identify groups of patients with similar characteristics, clinical variables of importance, as well as noise and outliers present in the data. The software also offers an easy-to-use interface that provides clients with clinically useful information and a visualization of risk factors linked to the risk prediction for an individual patient. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.