Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Patients were split into training, validation, and testing samples. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF).
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