ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment.Medical education online · 2025Article
- Utilizing natural language processing to analyze student narrative reflections for medical curriculum improvement.Medical teacher · 2025Article
- Review
- Comparing Scoring Consistency of Large Language Models with Faculty for Formative Assessments in Medical Education.Journal of general internal medicine · 2025Article
- The Global Use of Artificial Intelligence in the Undergraduate Medical Curriculum: A Systematic Review.Cureus · 2023Review
- Machine Scoring of Medical Students' Written Clinical Reasoning: Initial Validity Evidence.Academic medicine : journal of the Association of American Medical Colleges · 2021Article
- Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review.JMIR medical education · 2019Review
- Automated Comparison of Pathology Reports for On-the-job Assessment of Residents.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Competence is essential for health care professionals. Current methods to assess competency, however, do not efficiently capture medical students' experience. In this preliminary study, we used machine learning and natural language processing (NLP) to identify geriatric competency exposures from students' clinical notes. The system applied NLP to generate the concepts and related features from notes. We extracted a refined list of concepts associated with corresponding competencies. This system was evaluated through 10-fold cross validation for six geriatric competency domains: "medication management (MedMgmt)", "cognitive and behavioral disorders (CBD)", "falls, balance, gait disorders (Falls)", "self-care capacity (SCC)", "palliative care (PC)", "hospital care for elders (HCE)" - each an American Association of Medical Colleges competency for medical students. The systems could accurately assess MedMgmt, SCC, HCE, and Falls competencies with F-measures of 0.94, 0.86, 0.85, and 0.84, respectively, but did not attain good performance for PC and CBD (0.69 and 0.62 in F-measure, respectively).
Indexed as
Identifiers
25954341PMC4419906W2416868572What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.