Henry Musoke Semakula, Song Liang, Paul Isolo Mukwaya, Frank Mugagga, Denis Nseka, Hannington Wasswa, Patrick Mwendwa, Patrick Kayima, Simon Peter Achuu, Jovia Nakato
Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda
Malaria journal
mathematical models previously developed predict outcomes patients heart failure hf generally limited performance yet integrate complex data derived cardiopulmonary exercise testing cpet including breathbybreath data aimed develop validate timetoevent prediction model using deep learning framework using deepsurv algorithm predict outcomes hf inception cohort 2490 adult patients highrisk cardiac conditions hf underwent cpet breathbybreath measurements potential predictive features included known clinical indicators standard summary statistics cpets mathematical features extracted breathbybreath time series 13 measurements primary outcome composite death heart transplant mechanical circulatory support treated timetoevent outcomes predictive features ranked important included many features engineered breathbybreath data addition traditional clinical risk factors prediction model showed excellent performance predicting composite outcome area curve 093 training 087 validation data sets predicted vs actual freedom composite outcome calibration prediction model excellent model performance remained stable multiple subgroups patients using combined deep learning survival algorithm integrating breathbybreath data cpets resulted improved predictive accuracy longterm 10 years outcomes hf deepsurv opens door future prediction models highly performing fully use large complex quantity data generated care patients hf