AMIA Annu Symp Proc - Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.

Tópicos

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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
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{ gene(2352) biolog(1181) express(1162) }
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{ intervent(3218) particip(2042) group(1664) }
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{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

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).

Resumo Limpo

compet essenti health care profession current method assess compet howev effici captur medic student experi preliminari studi use machin learn natur languag process nlp identifi geriatr compet exposur student clinic note system appli nlp generat concept relat featur note extract refin list concept associ correspond compet system evalu fold cross valid six geriatr compet domain medic manag medmgmt cognit behavior disord cbd fall balanc gait disord fall selfcar capac scc palliat care pc hospit care elder hce american associ medic colleg compet medic student system accur assess medmgmt scc hce fall compet fmeasur respect attain good perform pc cbd fmeasur respect

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