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Frequent premature ventricular complexes (PVCs) can lead to adverse health conditions such as cardiomyopathy. The linear correlation between PVC frequency and heart rate (as positive, negative, or neutral) on a 24-hour Holter recording has been proposed as a way to classify patients and guide treatment with beta-blockers. Our objective was to evaluate the robustness of this classification to measurement methodology, different 24-hour periods, and nonlinear dependencies of PVCs on heart [...]

To develop an electronic medical record (EMR) data processing tool that confers clinical context to machine learning (ML) algorithms for error handling, bias mitigation, and interpretability.

Despite low-level evidence, acutely ill patients are often continuously monitored. This creates high false alarm rates and alarm fatigue with unclear clinical effectiveness. We compare metrics, including alarm burden, area under the receiver operator characteristic curve (auROC), sensitivity, and specificity for threshold, score (i.e., National Early Warning Score [NEWS]), and machine learning (ML) alarms.We retrospectively annotated continuous biometric data for acutely ill patients receiving hospital care at home for clinical [...]

Automation of clinical orders in electronic health records (EHRs) has the potential to reduce clinician burden and enhance patient safety. However, determining which orders are appropriate for automation requires a structured framework to ensure clinical validity, transparency, and safety.

Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis [...]

Building upon our previous work on predicting treatment retention in medications for opioid use disorder, we aimed to improve 6-month retention prediction in buprenorphine-naloxone (BUP-NAL) therapy by incorporating features derived from large language models (LLMs) applied to unstructured clinical notes.

Text embeddings are promising for semantic tasks, such as retrieval augmented generation (RAG). However, their application in health care is underexplored due to a lack of benchmarking methods. We introduce a scalable benchmarking method to test embeddings for health-care semantic tasks.

The objective of this study is to evaluate retrieval-augmented prediction for forecasting hospital length of stay (LOS) following surgery compared to traditional machine learning (ML), standalone large language models (LLMs), and retrieval-augmented generation (RAG) approaches.