OBJECTIVES: To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors that can impact their adoption, including overall performance, calibration, fairness, and resilience to privacy protections that reduce data fidelity. MATERIALS AND METHODS: We evaluated GPT-3.5, GPT-4, and traditional ML (as gradient-boosting trees) on clinical prediction tasks in EHR data from Vanderbilt University Medical Center (VUMC) and MIMIC IV. We measured predictive performance with area under the receiver operating characteristic (AUROC) and model calibration using Brier Score. To evaluate the impact of data privacy protections, we assessed AUROC when demographic variables are generalized. We evaluated algorithmic fairness using equalized odds and statistical parity across race, sex, and age of patients. We also considered the impact of using in-context learning by incorporating labeled examples within the prompt. RESULTS: Traditional ML [AUROC: 0.847, 0.894 (VUMC, MIMIC)] substantially outperformed GPT-3.5 (AUROC: 0.537, 0.517) and GPT-4 (AUROC: 0.629, 0.602) (with and without in-context learning) in predictive performance and output probability calibration [Brier Score (ML vs GPT-3.5 vs GPT-4): 0.134 vs 0.384 vs 0.251, 0.042 vs 0.06 vs 0.219)]. DISCUSSION: Traditional ML is more robust than GPT-3.5 and GPT-4 in generalizing demographic information to protect privacy. GPT-4 is the fairest model according to our selected metrics but at the cost of poor model performance. CONCLUSION: These findings suggest that non-fine-tuned LLMs are less effective and robust than locally trained ML for clinical prediction tasks, but they are improving across releases.

OBJECTIVE: Deep learning models for clinical event forecasting (CEF) based on a patient's medical history have improved significantly over the past decade. However, their transition into practice has been limited, particularly for diseases with very low prevalence. In this paper, we introduce CEF-CL, a novel method based on contrastive learning to forecast in the face of a limited number of positive training instances. MATERIALS AND METHODS: CEF-CL consists of two primary components: (1) unsupervised contrastive learning for patient representation and (2) supervised transfer learning over the derived representation. We evaluate the new method along with state-of-the-art model architectures trained in a supervised manner with electronic health records data from Vanderbilt University Medical Center and the All of Us Research Program, covering 48 000 and 16 000 patients, respectively. We assess forecasting for over 100 diagnosis codes with respect to their area under the receiver operator characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). We investigate the correlation between forecasting performance improvement and code prevalence via a Wald Test. RESULTS: CEF-CL achieved an average AUROC and AUPRC performance improvement over the state-of-the-art of 8.0%-9.3% and 11.7%-32.0%, respectively. The improvement in AUROC was negatively correlated with the number of positive training instances (P < .001). CONCLUSION: This investigation indicates that clinical event forecasting can be improved significantly through contrastive representation learning, especially when the number of positive training instances is small.

OBJECTIVE: Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users' granular interactions with patients' records by communicating various semantics and has been neglected in outcome predictions. MATERIALS AND METHODS: This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. RESULTS: The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919-0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860-0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. CONCLUSION: EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.