Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

BMC Musculoskeletal Disorders

Volumn 14, Issue , 2013, Pages

  Tseng, Wo Jan ^a   Hung, Li Wei ^a   Shieh, Jiann Shing ^b   Abbod, Maysam F ^c   Lin, Jinn ^a  

^a NATIONAL TAIWAN UNIVERSITY HOSPITAL   (Taiwan)

^b YUAN ZE UNIVERSITY   (Taiwan)

^c BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Artificial neural network; Calibration; Conditional logistic regression; Discrimination; Hip fracture

Indexed keywords

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FEMALE; HIP FRACTURE; HUMAN; MAJOR CLINICAL STUDY; MALE; MILK; MINI MENTAL STATE EXAMINATION; QUESTIONNAIRE; RISK ASSESSMENT; RISK FACTOR; WALKING DIFFICULTY;

AGE FACTORS; AGED; AGED, 80 AND OVER; CASE-CONTROL STUDIES; EFFECT MODIFIER, EPIDEMIOLOGIC; FEMALE; HIP FRACTURES; HUMANS; LIKELIHOOD FUNCTIONS; LOGISTIC MODELS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OSTEOPOROTIC FRACTURES; RISK ASSESSMENT; SEX FACTORS; TAIWAN;

EID: 84880110259     PISSN: None     EISSN: 14712474     Source Type: Journal    
DOI: 10.1186/1471-2474-14-207     Document Type: Article

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