AI Medical Compendium Topic:
Data Mining

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Leveraging Large Language Models for Synthetic Data Generation to Enhance Adverse Drug Event Detection in Tweets.

Studies in health technology and informatics
Adverse drug event (ADE) detection in social media texts poses significant challenges due to the informal nature of the text and the limited availability of annotations. The scarcity of ADE named entity recognition (NER) datasets for social media hin...

Leveraging Data Pipeline and LLM to Advance Patient Safety Event Studies.

Studies in health technology and informatics
Research utilizing the open-access MAUDE database frequently reveals unclear methodologies for extracting and processing medical device report (MDR) data, reducing reproducibility and consistency. By harnessing the OpenFDA API and our MAUDE extract-t...

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation.

Journal of chemical information and modeling
Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiri...

The influence of prompt engineering on large language models for protein-protein interaction identification in biomedical literature.

Scientific reports
Identifying protein-protein interactions (PPIs) is a foundational task in biomedical natural language processing. While specialized models have been developed, the potential of general-domain large language models (LLMs) in PPI extraction, particular...

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...

GPT-4 in Clinical Practice: Assessing Its Capability for Symptom Extraction from Cancer Patient Notes.

Studies in health technology and informatics
Accurate extraction of patient symptoms and signs from clinical notes is essential for effective diagnosis, treatment planning, and research. In this study, we evaluate the capability of GPT-4, specifically GPT-4o, in extracting symptoms and signs fr...

Evaluation of the Performance of a Large Language Model to Extract Signs and Symptoms from Clinical Notes.

Studies in health technology and informatics
Large language models (LLMs) have increasingly been used to extract critical information from unstructured clinical notes, which often include important details not captured in the structured sections of electronic health records (EHRs). This study a...

Applying Data Mining to Predict Perceived Benefits Risks of Robotics at Home for Dementia Caregiving Among African American Families.

Studies in health technology and informatics
We used data mining to predict the attitudes of 527 caregivers towards the pros and cons of using robotics and artificial intelligence (AI) for dementia care in African American families, with a focus on family-level factors. African American family ...

New opportunities and challenges for conservation evidence synthesis from advances in natural language processing.

Conservation biology : the journal of the Society for Conservation Biology
Addressing global environmental conservation problems requires rapidly translating natural and conservation social science evidence to policy-relevant information. Yet, exponential increases in scientific production combined with disciplinary differe...

Collaborative large language models for automated data extraction in living systematic reviews.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimic...