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MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery
Our objective was to determine whether two LLMs can serve information needs submitted by physicians as questions to an informatics consultation service in a safe and concordant manner.
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record
Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant.
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection
When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading.
Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
The algorithm discriminates mortality with an AUC of 0. 83 (95% CI 0. 79-0. 87) surpassing the discrimination of the RCRI score with an AUC of 0. 67 (CI 0. 61-0. 72) in the held out test cohort.
Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles
The growing demand for key healthcare resources such as clinical expertise and facilities has motivated the emergence of artificial intelligence (AI) based decision support systems.
