LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes
arXiv:2603.13673v2 Announce Type: replace Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging. However, this information is usually embedded in unstructured textual data rather than tabular data, making it difficult to be extracted accurately. We therefore propose LLM-MINE, a Large Language Model-based phenotype mining framework for automatic extraction of ADRD phenotypes from clinical notes. Using two expert-defined phenotype lists, we evaluate the extracted
The increasing sophistication of large language models makes them suitable for complex unstructured data extraction tasks in specialized domains like healthcare.
Accurate and automated extraction of disease phenotypes from clinical notes can significantly accelerate medical research, early diagnosis, and personalized treatment development for complex conditions.
The ability to efficiently mine unstructured clinical text for specific disease markers is significantly enhanced, moving beyond manual review or less sophisticated NLP methods.
- · AI healthcare startups
- · Pharmaceutical research
- · Medical data analytics
- · Patients with neurodegenerative diseases
- · Manual data extraction services
- · Legacy NLP solutions
Improved early detection and staging of Alzheimer's and related dementias due to automated phenotyping.
Accelerated development of new treatments and diagnostics as researchers gain better access to patient phenotype data.
Potential for predictive health models to identify individuals at high risk for ADRD years in advance, enabling prophylactic interventions.
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Read at arXiv cs.AI