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JAMIA Journal Club Webinar - January 2024

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JAMIA Journal Club
Inpatient nurses' preferences and decisions with risk information visualization Read the abstract Presenter Statement of Purpose Many hospitals have implemented sophisticated predictive models in hopes of improving care delivery; however, these systems’ effectiveness have yielded mixed results, with a primary reason being ineffective user interfaces contributing to implementation challenges. We [...]
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JAMIA Journal Club Webinar - December 2023

Type
JAMIA Journal Club
Improving model transferability for clinical note section classification models using continued pretraining Read the abstract Presenters Watch Recording Statement of Purpose Section classification refers to the task of assigning section names to clinical note segments. This is typically a preprocessing step that benefits downstream NLP tasks, such as named entity [...]
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JAMIA Journal Club Webinar - October 2023

Type
JAMIA Journal Club
Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO) Read the abstract Watch the Recording Presenters Statement of Purpose As the significance of Social Determinants of Health (SDoH) becomes increasingly acknowledged, non-medical factors surrounding us profoundly influence our health outcomes. In this growing field, our work serves [...]
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JAMIA Journal Club Webinar - September 2023

Type
JAMIA Journal Club
Automated detection of causal relationships among diseases and imaging findings in textual radiology reports Read the abstract Presenter Statement of Purpose Medical records are known to contain a wealth of information that can be used to better understand diseases and associated conditions. In particular, electronic health record (EHR) data can [...]
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JAMIA Journal Club Webinar - August 2023

Type
JAMIA Journal Club
More than algorithms: an analysis of safety events involving ML-enabled medical devices reported to the FDA Read the abstract Presenters Moderator Watch the Recording Statement of Purpose As machine learning (ML) enabled technologies are introduced into clinical care, it is crucial to evaluate their safety and efficacy. While the benefits [...]