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The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

Journal of biomedical informatics
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...

Automatic de-identification of electronic medical records using token-level and character-level conditional random fields.

Journal of biomedical informatics
De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informati...

Detection of sentence boundaries and abbreviations in clinical narratives.

BMC medical informatics and decision making
BACKGROUND: In Western languages the period character is highly ambiguous, due to its double role as sentence delimiter and abbreviation marker. This is particularly relevant in clinical free-texts characterized by numerous anomalies in spelling, pun...

Annotating risk factors for heart disease in clinical narratives for diabetic patients.

Journal of biomedical informatics
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm...

Injury narrative text classification using factorization model.

BMC medical informatics and decision making
Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently redu...

Normalization of relative and incomplete temporal expressions in clinical narratives.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives.

Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxe...

Quantifying care coordination using natural language processing and domain-specific ontology.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This research identifies specific care coordination activities used by Aging in Place (AIP) nurse care coordinators and home healthcare (HHC) nurses when coordinating care for older community-dwelling adults and suggests a method to quanti...

Leveraging LLMs to Understand Narratives in MAUDE Reports.

Studies in health technology and informatics
Interest in using the MAUDE database to investigate adverse events linked to medical devices has been growing. Yet, the narrative sections of these reports remain largely unexplored, leaving valuable insights unutilized and creating an incomplete und...

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.