AIMC Topic: Electronic Health Records

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Building a Natural Language Interface for FHIR Clinical Terminology Server.

Studies in health technology and informatics
While Fast Healthcare Interoperability Resources (FHIR) clinical terminology server enables quick and easy search and retrieval of coded medical data, it still has some drawbacks. When searching, any typographical errors, variations in word forms, or...

Early identification of patients at risk for iron-deficiency anemia using deep learning techniques.

American journal of clinical pathology
OBJECTIVES: Iron-deficiency anemia (IDA) is a common health problem worldwide, and up to 10% of adult patients with incidental IDA may have gastrointestinal cancer. A diagnosis of IDA can be established through a combination of laboratory tests, but ...

Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.

JCO clinical cancer informatics
PURPOSE: Data on end-of-life care (EOLC) quality, assessed through evidence-based quality measures (QMs), are difficult to obtain. Natural language processing (NLP) enables efficient quality measurement and is not yet used for children with serious i...

Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.

JCO clinical cancer informatics
PURPOSE: Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validate the application of an NLP model to an NSCLC cohort by extracting NSCLC concepts from free-text med...

Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System.

Journal of acquired immune deficiency syndromes (1999)
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
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
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
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
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
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.