9 citations · 14 across the 9 of their papers we have counts for
9 papers
Latent Knowledge as a Predictor of Fact Acquisition in Fine-Tuned Large Language Models
Daniel B. Hier, Tayo Obafemi-Ajayi
Large language models store biomedical facts with uneven strength after pretraining: some facts are present in the weights but are not reliably accessible under deterministic decod…
From Memorization to Generalization: Fine-Tuning Large Language Models for Biomedical Term-to-Identifier Normalization
Suswitha Pericharla, Daniel B. Hier, Tayo Obafemi-Ajayi
Effective biomedical data integration depends on automated term normalization, the mapping of natural language biomedical terms to standardized identifiers. This linking of terms t…
Predicting Failures of LLMs to Link Biomedical Ontology Terms to Identifiers Evidence Across Models and Ontologies
Daniel B. Hier, Steven Keith Platt, Tayo Obafemi-Ajayi
Large language models often perform well on biomedical NLP tasks but may fail to link ontology terms to their correct identifiers. We investigate why these failures occur by analyz…
Efficient Standardization of Clinical Notes using Large Language Models
Daniel B. Hier, Michael D. Carrithers, Thanh Son Do +1
Clinician notes are a rich source of patient information but often contain inconsistencies due to varied writing styles, colloquialisms, abbreviations, medical jargon, grammatical…
Mapping Biomedical Ontology Terms to IDs: Effect of Domain Prevalence on Prediction Accuracy
Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi
This study evaluates the ability of large language models (LLMs) to map biomedical ontology terms to their corresponding ontology IDs across the Human Phenotype Ontology (HPO), Gen…
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models
Daniel B. Hier, Thanh Son Do, Tayo Obafemi-Ajayi
Large language models (LLMs) have shown improved accuracy in phenotype term normalization tasks when augmented with retrievers that suggest candidate normalizations based on term d…
High-Throughput Phenotyping of Clinical Text Using Large Language Models
Daniel B. Hier, S. Ilyas Munzir, Anne Stahlfeld +2
High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of…
A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes
Syed I. Munzir, Daniel B. Hier, Chelsea Oommen +1
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EH…
High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models
Syed I. Munzir, Daniel B. Hier, Michael D. Carrithers
Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic healt…