Machine Learning (ML) applied to real-world data to guide prescription of immune-therapies in metastatic melanoma: a pilot study
Event: ISPOR Global 2023, Boston, MA, USA Authors: F. Gwadry-Sridhar, Billy Amzal View Poster Objectives: Newly diagnosed metastatic melanoma patients are often treated with molecular targeted therapy (TT) or with immunotherapy (IO). However, only a limited proportion of patients respond to those therapies, resulting in reduced survival and substantial inefficiencies for health systems. The purpose […]
Estimating the pertussis burden in adolescents and adults in the United States between 2007 and 2019
Publisher: Taylor & Francis OnlineAuthors: D. Macina, S. Mathur, M. Dvaretskaya, S. Ekhtiari, P. Hayat, M. Montmerle, C.Daluwatte View publication Objectives We developed a machine learning algorithm to identify undiagnosed pertussis episodes in adolescent and adult patients with reported acute respiratory disease (ARD) using clinician notes in an electronic healthcare record (EHR) database. Here, we utilized the algorithm to […]
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 […]
AI-Powered Pre-Competitive Disease and Care Modeling: Building a Reference Disease-Centric Framework for Value-Based Decision Making
Event: ISPOR 2023, Boston, MA, USA Authors: B. Amzal, A. Movschin, A. Chiorean,J. Tanniou, M. Rollot, M. Génin View Poster Objectives Drugs evaluation has been undergoing a transformational shift from the classical sequence of randomized control trials (RCT) and Real-World (RW) studies towards a more agile process, capitalizing better and faster from retrospective data. The purpose of this […]
Cluster Algorithm for Credible Subgroup Identification in Patients with Erectile Dysfunction Receiving Tadalafil: A Real-World Data Study
Event: ISPOR Global 2023, Boston, MA, USA Authors: KT. McVary, C. Daluwatte, RA Kloner, M. Montmerle, M. Rollot, P. Guilmin, M. Blanchon, N. Lambert, C. Esnault, A. Stewart, T. McGraw View Poster Objectives: This non-comparative, retrospective study aimed to characterize the profiles of erectile disfunction (ED) patients prescribed with tadalafil by clustering the homogenous subpopulations with different […]
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 […]
Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm
Publisher: Diabetes, Obesity and MetabolismAuthors: Angela Ibald-Mulli, Jochen Seufert, Julia M. Grimsmann, Markus Laimer, Peter Bramlage, Alexandre Civet, Margot Blanchon, Simon Gosset, Alexandre Templier, W. Dieter Paar, Fang Liz Zhou, Stefanie Lanzinger View publication Aim To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup […]
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, […]
LiSA : an assisted literature search pipeline for detecting serious adverse drug events with deep learning
Publisher: BMC Medical Informatics and Decision MakingAuthors: V. Martenot, V. Masdeu, J. Cupe, F. Gehin, M. Blanchon, J. Dauriat, A. Horst, M. Renaudin, P. Girard, J-D. Zucker View publication Introduction Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, […]
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 […]
