Through articles and studies, the Kaduceo team shares its knowledge in data science applied to the health sector
Kaduceo, co-author of work presented at the 24th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data
Explicability-based methods would allow access to new knowledge, to gain a greater comprehension of which characteristics and interactions really influence a couple's journey. These models can be used by practitioners…
Despite therapeutic advances, lung cancer remains the first cause of death from cancer. The main objective of this study was to identify risk factors associated with death within 3-months of…
Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods
This in-depth analysis determined significantly distinct typologies of inpatients, which facilitated definition of medical protocols to deliver the most appropriate cares for each profile.
Machine Learning analysis: Evolution of hospitalized patient characteristics through the first three COVID-19 waves
Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis
Evaluate the ability of an AI model (Deep Learning) to distinguish different eye pathologies to accelerate diagnosis
Obesity is strongly associated with many types of cancer. The goal of this study is to determine if bariatric surgery is associated with a lower risk of cancer.
Collaboration with CHI Créteil to study the impact of the establishment of a reference center for rare lung diseases on the care pathway of patients
Lyme disease is the most common vector-borne disease in France - its diagnosis sometimes leads to medical wanderings for patients and a significant cost for the healthcare system.
This study aims to analyze the causes of disruptions in pre-operative follow-up, identify the profiles of patients at risk and measure the economic impact of disruptions.
As machine learning (ML) is now widely applied in many fields, understanding what happens inside the black box is becoming a growing demand, especially by non-experts of these models.
This study analyzes trends in metabolic bariatric surgery among adolescents in France on the basis of national data over an 11-year period
In particular, this study reveals many similarities in the pattern of publications and procedures in bariatric surgery
To cope with the burden of disease, hospital staff were reassigned and elective surgery was significantly delayed. Various countries implemented national containment […].
Following initial hospitalization, re-admissions for pulmonary pathologies are among the conditions that generate the most readmission and consequently lead to additional expenditure on social security.
To reduce errors and better understand the predictions made by AI, the explicability of AI models (XAI for "eXplainable AI") has emerged as a research field.
Automatic image analysis has seen its performance grow strongly in recent years. These recent advances improve the construction of predictive imaging models, increasing their reliability
Our journey design begins with the first recording of a patient for a reason in a healthcare facility until the last event of their management: Time sequence of all care…
The study of hospital readmission could contribute to the improvement of care paths but the subject is quite complex. The different models in the literature are difficult to compare. To…
Faced with this health crisis, it was natural for Kaduceo to make our expertise available to hospitals. Adapting some of our predictive and indicator models to allow hospitals to have…
From examples where each image is associated with a category, the so-called Machine Learning models learn to identify patterns specific to the observations of the same category, with the aim…
Lung Cancer: Analyze the profile and health care facility transitions of patients with lung cancer to infer elements that characterize early death
Pathway Comparison: Physicians Would Like to Learn from Muco Patient Management to Enrich PCD Patient Management
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