Development of an algorithm for finding pertussis episodes in a population-based electronic health record database
Publisher: Taylor & Francis OnlineAuthors: C. Daluwatte, M. Dvaretskaya, S. Ekhtiari, P. Hayat, M. Montmerle, S. Mathur, D. Macina View publication Background While tetanus-diphtheria-acellular pertussis (Tdap) vaccines for adolescents and adults were licensed in 2005 and immunization strategies proposed, the burden of pertussis in this population remains under-recognized mainly due to atypical disease presentation, undermining efforts to optimize protection […]
Cluster analysis of kidney function decline among males with Fabry disease in a large United States electronic health records database
Publisher: ndt (Nephrology Dialysis Transplantation)Authors: A. Chiorean, N. Lyn, S. Kabadi, M. Blanchon, P. Hayat, P. Loustalot, M. Maski, M. Montmerle, E. Ponce View publication Background Fabry disease (FD) is an X-linked lysosomal storage disorder caused by deficient α-galactosidase A activity. The spectrum of disease includes phenotypes ranging from ‘classic’ to ‘later-onset’, with varying kidney disease progression. Identifying patterns of declining kidney function […]
Qluster: An easy-to-implement generic workflow for robust clustering of health data
Publisher: Frontiers in Artificial IntelligenceAuthors: C. Esnault, M. Rollot, P. Guilmin, J-D. Zucker View publication Background The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, […]
Modeling approaches for early warning and monitoring of pandemic situations as well as decision support
Publisher: Frontiers in Public HealthAuthors: J. Botz, D. Wang, N. Lambert, N. Wagner, M. Génin, E. Thommes, S. Madan, L. Coudeville, H.Fröhlich View publication The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and […]
Bioinformatics modelling of baseline characteristics to predict clinical outcomes and responses in the LixiLan-L trial
Event: International Diabetes Federation (IDF) Congress, Abu Dhabi, UAE Authors: P. Fogel, A. Civet, T. Fan, A. Shaunik, C. Brulle-Wohlhueter, Y. Gaston-Mathe View Poster Background Identifying categories of patients with diabetes sharing common characteristics and analysing clinical outcomes in these groups may lead to better understanding of patient diversity and generate hypotheses on disease-driving mechanisms. […]
Using machine learning algorithms to identify predictive factors of clinical outcomes with iGlarLixi or iGlar in the LixiLan-L trial
Event: Annual Meeting of the European Association for the Study of Diabetes (EASD) 2017, Lisbon, Portugal Authors: Y. Gaston-Mathé, T. Fan, A. Shaunik, C.Brulle-Wohlhueter, A. Civet View Poster
