Essam H Houssein, Mohamed H Hassan, Mohamed A Mahdy, Salah Kamel
Development and application of equilibrium optimizer for optimal power flow calculation of power system
Applied intelligence (Dordrecht, Netherlands)
machine learningbased approaches seek accomplish individualized treatment effect prediction gained traction however salient challenges lack wider recognition describe key methodologic considerations individualized treatment effect prediction models using data treatment preserved cardiac function heart failure aldosterone antagonist trial spironolactone heart failure preserved ejection fraction causal survival forest algorithm used model development calibration discrimination evaluated using bootstrappingbased internal validation procedure observed benefits described predicted benefit quartiles quartiles known effect modifier ejection fraction negative control analysis noncardiovascular death outcome implemented detect confounding among 3445 participants 671 events occurred median 33 years followup internal validation higher average observed benefit noted among patients highest quartile predicted benefit median interquartile range observed restricted mean survival time difference 33 years highest quartile modelpredicted benefit 62 days 32 83 47 days 26 67 lowest quartile ejection fraction bodymass index higher contribution prediction benefit relative included measures 337 vs glomerular filtration rate 273 ejection fraction 151 younger age 128 benefit observed noncardiovascular death higher modelpredicted benefit quartiles although benefit noncardiovascular death observed lower quartiles carefully applied validated predictive models hold promise identifying heterogeneous treatment effects useful hypothesis generation regarding role phenotypic characteristics modifying benefit experimental interventions clinical trials funded national heart lung blood institute clinicaltrialsgov number nct00094302