Ayush Chadha, Theresa L Charrois, Jill Hall
Moving beyond professionalism: Pharmacy students understanding of professionalism and professional identity
Currents in pharmacy teaching & learning
osteoporotic fractures significantly impact individualss quality life exert substantial pressure social pension system study aims develop prediction models osteoporotic fracture uncover potential risk factors based electronic health records ehr data patients osteoporosis extracted ehr xinhua hospital july 2012october 2017 demographic clinical features used develop prediction models based 12 independent machine learning ml algorithms 3 hybrid ml models facilitate nuanced interpretation results comprehensive importance score conceived incorporating various perspectives effectively discern mine critical features data total 8530 patients osteoporosis included analysis 1090 cases 128 fracture patients hybrid model synergistically combines support vector machine svm xgboost algorithms demonstrated best predictive performance terms accuracy precision 90 among benchmark models blood calcium alkaline phosphatase alp creactive protein crp apolipoprotein ab ratio highdensity lipoprotein cholesterol hdlc statistically found associated osteoporotic fracture hybrid machine learning model reliable tool predicting risk fracture patients osteoporosis expected assist clinicians identifying highrisk fracture patients implementing early interventions