Through articles and studies, the Kaduceo team shares its knowledge in data science applied to the health sector
A Prospective Pharmacoepidemiologic Cohort Study in 30 French NICUs From 2014 to 2020
No consensus exists about the doses of analgesics, sedatives, anesthetics, and paralytics used in critically ill neonates.
Comparison of explanatory methods: influence of characteristics
Kaduceo, co-author of work presented at the 24th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data
Comparison of predictive models for cumulative live birth rate after treatment with ART
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…
Risk factors for early mortality of lung cancer patients in France: A nationwide analysis
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
Ophthalmology: Image classification based on a Deep Learning model to accelerate diagnosis
Evaluate the ability of an AI model (Deep Learning) to distinguish different eye pathologies to accelerate diagnosis
Effect of bariatric surgery on cancer risk
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.
Impact of the establishment of a reference center for rare respiratory diseases
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: A Cost-Effectiveness Study of Management (FR)
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.
Bariatric Surgery: Analyse the care pathway and predict the risk of discontinuation
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.
Coalitional Strategies for Efficient Individual Prediction Explanation
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.
Trends in metabolic bariatric surgery in adolescents in France
This study analyzes trends in metabolic bariatric surgery among adolescents in France on the basis of national data over an 11-year period
Comparison of Surgical Activity and Scientific Publications in Bariatric Surgery
In particular, this study reveals many similarities in the pattern of publications and procedures in bariatric surgery
Impact of COVID-19 on surgical emergencies: nationwide analysis
To cope with the burden of disease, hospital staff were reassigned and elective surgery was significantly delayed. Various countries implemented national containment […].
Prediction of unplanned readmissions
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.
State of art: Explainability of AI models
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.
Medical imaging: Explainability methods for image classification
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…
State of the art: Prediction of hospital readmission
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…
Covid-19 : Predicting admissions and extracting valuable insights from data analytics
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…
Neural Network Architectures for image classification
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 : How to predict an early death ?
Lung Cancer: Analyze the profile and health care facility transitions of patients with lung cancer to infer elements that characterize early death
Respiratory Diseases: Comparison of Care Pathways
Pathway Comparison: Physicians Would Like to Learn from Muco Patient Management to Enrich PCD Patient Management
Emergency Activity Prediction AI Model
The main challenge is to be able to provide forecasts that are reliable enough to alert hospitals in the event of a peak of activity. We are looking to propose…
An idea ? A project ?
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