Adoption of statistical methods for recurrent events may offer benefits in future investigations in multiple sclerosis (MS) research and clinical trials – ECTRIMS 2024
Quentin Pilard (Senior Biostatistician) and David Herman (Biostatistician) are presenting our poster entitled “Analyzing Recurrent Events in Multiple Sclerosis: A Review of Statistical Models with Application to MSOAC Trial” at ECTRIMS 2024 (European Committee for Treatment and Research in Multiple Sclerosis) in Copenhagen, Denmark.
Machine learning achieves promising performance in early diagnosis of rare diseases
Delays in the diagnosis of rare genetic conditions such as acid sphingomyelinase deficiency (ASMD) highlight the necessity for diagnostic algorithms to help clinicians identify patients at high risk of such diseases. To address this, electronic health records (EHRs) and machine learning (ML) were used to develop an ASMD diagnostic algorithm, which achieved promising results. In […]
Enhancing Patient-Centered Decision-Making: Quinten Health’s contributions to the More-EUROPA Project
In efforts to push for the use of registry data to support patient-centered decision-making in regulatory and health technology assessment (HTA) processes, Quinten Health, expert in AI and data science for healthcare, is taking part in two work packages (WP) within the More-EUROPA consortium. The first is to assess the level of evidence and uncertainty around the use of registry data for pre-licensing exploratory objectives; and the second is leading the design of an automated tool to identify, select and assess fit-for-purpose registries and data elements.
AIOLOS project’s Minimum Viable Product (MVP) showcased at ISPOR EU 2023
We had the occasion to highlight the collective initiatives of the AIOLOS project with an informative poster at ISPOR Europe 2023. This poster aims to provide an overview of the accomplishments achieved within the first year, presenting the successful development of a Minimum Viable Product (MVP).
A promising algorithm for early diagnosis and intervention opportunities in Rare Diseases
A promising algorithm for early diagnosis of Acid Sphingomyelinase Deficiency (ASMD) in patients with unexplained Interstitial Lung Disease (ILD). Leveraging Electronic Health Records (EHR) and machine learning, a ASMD diagnostic algorithm, was developed achieving remarkable results. Indeed, it distinguished ASMD patients from a general population with a sensitivity of 80% and a specificity of over […]
Machine learning algorithm identifies undiagnosed episodes of pertussis
Experts at Quinten Health have developed a supervised machine learning algorithm to identify undiagnosed episodes of pertussis in adolescents and adults reporting acute respiratory illness. As pertussis is often misidentified, misdiagnosed or misreported, it is complex to assess its incidence rate in the above-mentioned populations. The aim was therefore to better estimate the incidence in […]
Developing a machine learning algorithm to detect undiagnosed pertussis episodes
Quinten Health has developed a machine learning algorithm to identify misdiagnosed or undiagnosed episodes of whooping cough in patients diagnosed with acute respiratory illness. The team used elements from clinician notes such as observed symptoms, and demographic information from electronic care records from Optum Humedica, a US population-based database. Although tetanus-diphtheria-acellular pertussis (Tdap) vaccines for […]
Quinten Health’s poster selected for presentation at AFCROs’ symposium
Quinten Health’s team presented their poster “Artificial intelligence for real-life disease modeling: vision, examples and impact on drug evaluation” at the 15th edition of the AFCROs’ symposium on Real-World health data. Marie Génin & Sam Ekhtiari were there on June 20th to represent the team where the poster was displayed.Billy Amzal, Antoine Movschin, Mélissa Rollot, […]
AliBERT : the first pretrained language model for French biomedical text
The paper “AliBERT : A pretrained language model for French biomedical text” was written in collaboration with Aman Berhe, Guillaume Draznieks, Vincent Martenot, Valentin Masdeu, Lucas Davy and Jean-Daniel Zucker. BERT architecture, which allow for context learning on text documents, is mostly trained on common English text resources.Performances in other languages, especially in specific topics which requires deep knowledge and vocabulary, are […]
Real-World Data analysis on Fabry’s disease provide insights that may contribute to a more personalized patient care
The article “Cluster analysis of kidney function decline among males with Fabry disease in a large United States electronic health records database” has been published on April 14th 2023 in the Oxford Academic Journal on behalf of the European Renal Association. It was written in collaboration with Alexandra Chiorean, Nicole Lyn, Shaum Kabadi, Margot Blanchon, […]
