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, […]
Identifying predictive factors of diabetic ketoacidosis using the Qfinder algorithm
The article “Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm” was written in collaboration with 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. Diabetic ketoacidosis (DKA) is identified […]
Our new clustering method “Qluster” is out!
Discover our new article “Qluster” referring to a generic and robust method of clustering health data. Already applied on many projects, this method allows to generate a relevant segmentation of your health data with robust and referenced methods, while ensuring an interpretability of the results. Guidelines are also provided in order to make the most […]
Discover our new work around patients’ perception understanding throught NLP: a poster presented at ispor EU 2022!
The poster ” (# ) ’ ” was written in collaboration with Mélissa Rollot, Coriande Clemente, Vincent Martenot and Paul Hayat of Quinten and Quinten Health. The presented method makes possible the automated and efficient extraction of topics from a large volume of text data : Applied to patients’ testimonies, such analysis provides strong insights on patients’ perception about […]
