Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.
AMIA Annu Symp Proc
; 2014: 375-84, 2014.
Article
en En
| MEDLINE
| ID: mdl-25954341
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).
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Competencia Clínica
/
Educación de Pregrado en Medicina
/
Evaluación Educacional
/
Geriatría
Tipo de estudio:
Prognostic_studies
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
AMIA Annu Symp Proc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2014
Tipo del documento:
Article
País de afiliación:
Túnez