Rrahim Sejdiu, Blertë Sylejmani, Lulzim Idrizi, Agron Bajraktari, Muharrem Sejdiu
Discrepancy between pupils' body and classroom furniture in elementary schools: A case study in the Republic of Kosovo
Work (Reading, Mass.)
prediction inhibitor development patients hemophilia ha remains challenge construct predictive model inhibitor development ha using network clinical variables biomarkers based individual similarity network previously untreated minimally treated children severemoderatelysevere ha participants hemfil cohort study followedup reaching 75 exposure days ed without inhibitor inh upon inhibitor development inh clinical data biological samples collected start factor viii fviii replacement t0 predictive model hemfilnet built compare networks potential global topological differences inh inh t0 considering network robustness validation leaveoneout crossvalidation technique employed accuracy precision recall f1score used evaluation metrics machinelearning model included 95 children ha cha 31 33 developed inhibitors algorithm featuring 37 variables identified distinct patterns networks t0 inh inh accuracy model 742 cha inh 984 inh focusing analysis cha highrisk f8 mutations inhibitor development accuracy identifying cha inh increased 821 machinelearning algorithm demonstrated overall accuracy 905 predicting inhibitor development cha improved restricting analysis cha highrisk f8 genotype however model requires validation cohorts yet missing data variables hindered precise predictions