Leveraging Natural Language Processing, Machine Learning, and Social Determinants in Electronic Health Records to Improve Opioid Use Disorder Clinical Care

Health Records to Improve Opioid Use Disorder Clinical Care 

The purpose of the panel is to discuss and inform the researchers and practitioners about the differences between task-specific models versus general-purpose models, namely, between Named Entity Recognition (NER) models and Large Language Models (LLM). NER is an important task in natural language processing. Due to the complexity and ever-changing nature of language, recognizing a named entity requires a significant effort in model training in addition to the pre-trained models. LLMs on the other hand, do not require such effort. The panel will elucidate the differences between task-specific NER models and LLM on entity recognition tasks and showcase the comparisons between the two models in various use cases. In addition, the panel will offer tips and lessons learned from working with both task-specific NER and LLM in a large corpus of texts.

Learning Objectives

Identify key differences between Named Entity Recognition (NER) models and Large Language Models (LLMs) in training, adaptability, and performance. Analyze the effectiveness of NER models and LLMs across different use cases. Apply best practices for selecting, fine-tuning, and deploying NER models and LLMs.

Moderator

• Elise BerlinerOracle Life Sciences

Speakers

• Elise Berliner (Icahn School of Medicine at Mount Sinai)
• David Talby, Dr. (John Snow Labs)
• Hongfang Liu, PhD (University of Texas Health Science Center at Houston)

Continuing Education Credit

Physicians

The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

The American Medical Informatics Association designates this online enduring material for 1.25 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.

ACHIPs^TM

AMIA Health Informatics Certified Professionals^TM (ACHIPs^TM) can earn 1 professional development unit (PDU) per contact hour.

ACHIPs^TM may use CME/CNE certificates or the ACHIPs^TM  Recertification Log to report 2025 Summit sessions attended for ACHIPs^TM Recertification.

Claim credit no later than within three years of the release date or within two years of your purchase date, whichever is sooner. 

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