Through articles and studies, the Kaduceo team shares its knowledge in data science applied to the health sector
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
An idea ? A project ?
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