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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.

ACHIPsTM

AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.

ACHIPsTM may use CME/CNE certificates or the ACHIPsTM  Recertification Log to report 2024 Symposium sessions attended for ACHIPsTM Recertification.

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.

FAQs

All content was recorded live at AMIA’s Annual Symposium event November 9-13, 2024, in San Francisco, CA. Plan now to join us for the next Annual Symposium!

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Purchase the AMIA 2024 Annual Symposium On Demand Bundlefor the best value on all top 20 sessions. Additional individual sessions are also available for purchase in the catalog.

Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.

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Yes! You can claim Self-Study credit when you complete AMIA 2024 Annual Symposium On Demand sessions, in addition to claiming Live credit for attending the live event. View the full details on self-study accreditation for this product.

Yes, The AMIA 2024 Annual Symposium On Demand Bundle (Presenter, Slides, and Audio) may be purchased for 8 educational credits using your health system’s code at checkout. Individual sessions (Presenter, Slides, and Audio) may be purchased for 1 educational credit per session using your health system’s code at checkout.

Available Until:
Dates and Times:
Type: AMIA On Demand
Course Format(s): On Demand
Credits:
1.25
CME
Price: Member: $60, Nonmember: $85
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