Hospital-Based Medicine

Automated Insomnia Phenotyping from Electronic Health Records: Leveraging Large Language Models to Decode Clinical Narratives

Insomnia is a highly prevalent but often underdiagnosed condition in clinical practice. Its inconsistent documentation in electronic health records (EHRs) limits population-level analyses and obstructs efforts to evaluate treatment patterns or outcomes. We present a novel, fully automated approach for phenotyping insomnia directly from unstructured clinical notes using generative large language mo...

Enhancing Cause of Death Prediction: Development and Validation of ML Models Using Multimodal Data Across Multiple Healthcare Sites

Timely and accurate determination of causes of death (CoD) is essential for public health surveillance, epidemiological research, and healthcare policy development. However, obtaining up-to-date and detailed CoD information is challenging due to delays in official death records and inconsistencies in data reporting across institutions. To develop and validate machine learning (ML) models capable o...

Urethra contours on MRI: multidisciplinary consensus educational atlas and reference standard for artificial intelligence benchmarking

The urethra is a recommended avoidance structure for prostate cancer treatment. However, even subspecialist physicians often struggle to accurately id...

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study

Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...

Advancing In-Hospital Mortality Prediction for Acute Myocardial Infarction: an analysis from the American Heart Association Get-With-the-Guidelines Coronary Artery Disease Registry

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with acute myocardial infarction (AMI) contributing to over 100,000 dea...

Zero-Shot Large Language Models for Long Clinical Text Summarization with Temporal Reasoning

Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajecto...

Privacy Protection for Chinese Electronic Medical Records Using Large Language Models: Effectiveness Evaluation and Application of LLM Models in Medical Data Tasks

The privacy protection of medical patients has remained a critical concern in healthcare information management during the digital era. Conventional a...

Evaluation of Large Language Model-Generated Patient Information for Communicating Radiation Risk

Large language models are increasingly used to generate patient information in healthcare. However, their ability to communicate complex topics, such ...

clickBrick Prompt Engineering: Optimizing Large Language Model Performance in Clinical Psychiatry

Prompt engineering has the potential to enhance large language models’ (LLM) ability to solve tasks through improved in-context learning. In clinical ...

AI-generated patient-friendly discharge summaries to empower patients

Patients often struggle to fully understand their discharge letters after inpatient hospital stays, which are often replete with domain-specific medic...

Large Language Models Improve Coding Accuracy and Reimbursement in a Neonatal Intensive Care Unit

Diagnosis coding is essential for clinical care, research validity, and hospital reimbursement. In neonatal settings, manual coding is frequently erro...

Developing and validating an explainable digital mortality prediction tool for extremely preterm infants

Decision-making in perinatal management of extremely preterm infants is challenging. Mortality prediction tools may support decision-making. We used p...

Portability of an artificial intelligence model for self-harm detection across hospital settings

Adequate self-harm surveillance is a key part of suicide prevention efforts. Our prior work has demonstrated the efficacy of an artificial intelligenc...

Leveraging Large Language Models and Patient Portal Messages for Early Identification of Depression

Large language model (LLM)-assisted early warning system may help overcome existing barriers to timely depression diagnosis in patients with cardiovas...

Personalized, closed-loop deep brain stimulation for chronic pain

Chronic pain is a major healthcare problem associated with maladaptive brain circuit changes - many patients are unresponsive to all available therapi...

Development and Validation of VC-MAES and VC-SEPS: Deep Learning-Based Early Warning Systems for Hospitalized Patients

The timely detection of ward deterioration—including unplanned intensive care unit (ICU) transfer, cardiac arrest, death, and sepsis—remains an unmet ...

External Validation of a Machine Learning Model to Predict Postpartum Hemorrhage in a US Northeastern Healthcare System

Postpartum hemorrhage (PPH) is a major cause of maternal morbidity and mortality. Timely prediction may prevent adverse maternal outcomes, and efforts...

Risk Prediction Modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: Leveraging Machine Learning

Pre-procedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and bench...

A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice

Ambient artificial intelligence (AI) offers the potential to reduce documentation burden and improve efficiency through clinical note generation. Wide...

A Better Way: Initial Acceptability Testing of Using Artificial Intelligence Tools to Accelerate Development of Trauma Clinical Guidance

Representatives of the trauma community have voiced a need for a new approach to developing clinical guidance. In this study, we test the initial acce...