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We evaluate the effectiveness of large language models (LLMs), specifically GPT-based (GPT-3.5 and GPT-4) and Llama-2 models (13B and 7B architectures), in autonomously assessing clinical records (CRs) to enhance medical education and diagnostic skills.

To develop indices of US hospital interoperability to capture the current state and assess progress over time.

Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care.

With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid [...]

To describe the prevalence of and trends in practices that interfere with the exchange of patient health information (potential information blocking) 2 years after implementation of information-blocking regulations.

Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.

To identify distinct patterns in consumer willingness to share health data with various stakeholders and analyze characteristics across consumer groups.

Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous.

To develop a distributed algorithm to fit multi-center Cox regression models with time-varying coefficients to facilitate privacy-preserving data integration across multiple health systems.

We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).