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Computable Phenotypes

Type
AMIA On Demand
Credits
1.50
CME
Real-World Computable Phenotypes of Patient-Reported Disability in Multiple Sclerosis Common tools to measure multiple sclerosis (MS) disability are rarely available in the real-world clinical setting. Leveraging electronic health records (EHR) data and disability outcomes from two independent EHR-linked MS research registries, we aimed to develop test and validate computable phenotypes [...]
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NEW
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Incorporating Natural Language Processing within a Large National Network: Current State of ENACT NLP Working Group

Type
AMIA On Demand
Credits
1.25
CME
The Evolve to Next-Gen Accrual to Clinical Trials (ENACT) (previously known as ACT) network was established in 2015 with funding from the NCATS. ENACT is a large federated network of EHR data repositories at 57 CTSA hubs that serves as an information superhighway for querying EHR data on >142M patients [...]
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NEW
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Innovation in Health Information Networks to Propel Participant-Authorized Precision Medicine Research

Type
AMIA On Demand
Credits
1.25
CME
The National Institutes of Health All of Us Research Program has launched an innovative pilot to inform U.S. healthcare interoperability leaders in translational science on the use of health information exchanges (HIEs) and health information networks (HINs) in participant-consented research studies. The panel will share perspectives on the technical and [...]
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NEW
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Leveraging Natural Language Processing, Machine Learning, and Social Determinants in Electronic Health Records to Improve Opioid Use Disorder Clinical Care

Type
AMIA On Demand
Credits
1.25
CME
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 [...]
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NEW
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Modeling Complex Real World Data

Type
AMIA On Demand
Credits
1.00
CME
Unraveling Complex Temporal Patterns in EHRs via Robust Irregular Tensor Factorization Electronic health records (EHRs) contain diverse patient data with varying visit frequencies, resulting in unaligned tensors in the time mode. While PARAFAC2 has been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and [...]
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