AIMC Topic: Natural Language Processing

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Development and Validation of a Natural Language Processing Algorithm for Extracting Clinical and Pathological Features of Breast Cancer From Pathology Reports.

JCO clinical cancer informatics
PURPOSE: Electronic health records (EHRs) are valuable information repositories that offer insights for enhancing clinical research on breast cancer (BC) using real-world data. The objective of this study was to develop a natural language processing ...

Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.

JCO clinical cancer informatics
PURPOSE: Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with natural language processing (NLP). However, more efficient NLP systems are needed to detect various symptoms and distinguish observed symptoms from neg...

Insights Into the Patient Experience of Hormone Therapy for Early Breast Cancer Treatment Using Patient Forum Discussions and Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: Understanding the real-world experience of patients with early breast cancer (eBC) is imperative for optimizing outcomes and evolving patient care. However, there is a lack of patient-level data, hindering clinical development. This social l...

Artificial intelligence and headache.

Cephalalgia : an international journal of headache
BACKGROUND AND METHODS: In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a c...

Surveillance of Health Care-Associated Violence Using Natural Language Processing.

Pediatrics
BACKGROUND AND OBJECTIVES: Patient and family violent outbursts toward staff, caregivers, or through self-harm, have increased during the ongoing behavioral health crisis. These health care-associated violence (HAV) episodes are likely under-reported...

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring t...

A general framework for developing computable clinical phenotype algorithms.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machi...

Improving biomedical entity linking for complex entity mentions with LLM-based text simplification.

Database : the journal of biological databases and curation
Large amounts of important medical information are captured in free-text documents in biomedical research and within healthcare systems, which can be made accessible through natural language processing (NLP). A key component in most biomedical NLP pi...

Harnessing large language models' zero-shot and few-shot learning capabilities for regulatory research.

Briefings in bioinformatics
Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's Ch...