Publications
Machine learned decision tree for diagnosis of ASM...
Machine learning on electronic health records (EHR) was used to produce a data-driven decision tree (algorithm) to flag high-risk patients for ASMD...
Machine Learning (ML) applied to real-world data t...
Newly diagnosed metastatic melanoma patients are often treated with molecular targeted therapy (TT) or with immunotherapy (IO).
Estimating the pertussis burden in adolescents and...
We developed a machine learning algorithm to identify undiagnosed pertussis episodes in adolescent and adult patients with reported acute respirato...
Development of an algorithm for finding pertussis ...
While tetanus-diphtheria-acellular pertussis (Tdap) vaccines for adolescents and adults were licensed in 2005 and immunization strategies proposed,...
AI-Powered Pre-Competitive Disease and Care Modeli...
Drugs evaluation has been undergoing a transformational shift from the classical sequence of randomized control trials (RCT) and Real-World (RW) st...
Cluster Algorithm for Credible Subgroup Identifica...
This non-comparative, retrospective study aimed to characterize the profiles of erectile disfunction (ED) patients prescribed with tadalafil by clu...
Cluster analysis of kidney function decline among ...
Fabry disease (FD) is an X-linked lysosomal storage disorder caused by deficient α-galactosidase A activity. The spectrum of disease includes phen...
Identification of Predictive Factors of Diabetic K...
To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algor...
Qluster: An easy-to-implement generic workflow for...
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compo...
ALIBERT: A PRETRAINED LANGUAGE MODEL FOR FRENCH BI...
Over the past few years, domain specific pretrained language models have been investigated and have shown remarkable achievements in different down...
LiSA : An assisted literature search pipeline for ...
Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the vo...
A Natural Language Processing (NLP) Approach to Au...
Patients’ testimonials (e.g. posts on forums or responses to questionnaires) provide valuable insights to define and characterize patient-reporte...
Modeling approaches for early warning and monitori...
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations
Epidemiology, treatment patterns, clinical outcome...
Immune‐mediated thrombotic thrombocytopenic purpura (iTTP) is a life‐threatening thrombotic microangiopathy. Due to its rarity, epidemiology an...
Rationale and methodology for a European pooled an...
Type 2 diabetes mellitus (T2DM) is a common and heterogeneous disease. Using advanced analytic approaches to explore real-world data may identify d...
Using natural language processing (NLP) to underst...
IDMPS is an international, observational study to assess care practices and clinical profiles of people with diabetes in developing countries.
Identification of predictive factors of DKA using ...
Diabetic ketoacidosis (DKA) is a serious complication of type 1 diabetes (T1D), which is difficult to diagnose due to variability in symptoms.
Unmet medical needs in people with type 2 diabetes...
Limited data exist on the impact of different insulin therapy regimes on outcomes in people with diabetes. The current study describes the unmet me...
Identification of subgroups of patients with Type ...
To investigate the effects of sodium-glucose co-transporter-2 (SGLT2) inhibitors vs. dipeptidyl peptidase-4 (DPP-4) inhibitors on renal function pr...
A machine learning algorithm can identify clusters...
Detecting consistent patterns of interest can be performed using data-driven subgroup discovery algorithms. These may be instrumental in exploiting...