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View article: Large Language Models for Adverse Drug Events: A Clinical Perspective
Large Language Models for Adverse Drug Events: A Clinical Perspective Open
Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), includin…
View article: Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model
Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model Open
Combined use of this AI model with the manual review of clinical notes will improve both sensitivity and specificity in the detection of irAEs, decreasing workload and costs and facilitating the development of improved immunotherapies.
View article: Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records
Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records Open
We assessed the generalizability of machine learning methods using natural language processing (NLP) techniques to detect adverse drug events (ADEs) from clinical narratives in electronic medical records (EMRs). We constructed a new corpus…
View article: Correction: PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature
Correction: PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature Open
View article: PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature
PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature Open