AIMC Topic: Electronic Health Records
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Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System.
Journal of acquired immune deficiency syndromes (1999)
Sep 1, 2024
BACKGROUND: Effective measures exist to prevent the spread of HIV. However, the identification of patients who are candidates for these measures can be a challenge. A machine learning model to predict risk for HIV may enhance patient selection for pr...
The first step is the hardest: pitfalls of representing and tokenizing temporal data for large language models.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVES: Large language models (LLMs) have demonstrated remarkable generalization and across diverse tasks, leading individuals to increasingly use them as personal assistants due to their emerging reasoning capabilities. Nevertheless, a notable o...
Disambiguation of acronyms in clinical narratives with large language models.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVE: To assess the performance of large language models (LLMs) for zero-shot disambiguation of acronyms in clinical narratives.
Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology findings.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVES: To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (A...
Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVES: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline th...
Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVE: To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.
A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVES: This comparative analysis aims to assess the efficacy of encoder Language Models for clinical tasks in the Spanish language. The primary goal is to identify the most effective resources within this context.
Clinical risk prediction using language models: benefits and considerations.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVE: The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipe...
Improving large language models for clinical named entity recognition via prompt engineering.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based str...
Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.
Studies in health technology and informatics
Aug 30, 2024
INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exceptio...
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