title: Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.
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).
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.
authors.forename: Yukun
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.
authors.forename: Jesse
authors.initials: J
authors.lastname: Wrenn
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX ; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN ; Department of Medicine, Vanderbilt University, Nashville, TN.
authors.forename: Anderson
authors.initials: A
authors.lastname: Spickard
authors.affiliation: Department of Medicine, Vanderbilt University, Nashville, TN.
authors.forename: Ralf
authors.initials: R
authors.lastname: Habermann
authors.affiliation: Department of Medicine, Vanderbilt University, Nashville, TN.
authors.forename: James
authors.initials: J
authors.lastname: Powers
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN ; Department of Medicine, Vanderbilt University, Nashville, TN.
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
mesh_terms: D019540:Area Under Curve; D001185:Artificial Intelligence; D002983:Clinical Competence; D004504:Education, Medical, Undergraduate / Q000592:standards*; D004521:Educational Measurement / Q000379:methods*; D005853:Geriatrics / Q000193:education*; D006801:Humans; D009323:Natural Language Processing; D013337:Students, Medical; D013714:Tennessee