Publications by authors named "Mikael Azoulay"
Background: The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a prognostic model for solid tumour patients using AI on multimodal data.
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- Early discontinuation in early-phase oncology trials impacts over one third of patients and complicates study timelines and costs; the research aims to predict successful completion of screening and dose-limiting toxicity periods using automated report analysis.
- A machine learning model was developed using a large dataset of consultation reports to predict patient outcomes, achieving solid performance metrics (F1 score 0.80, recall 0.81) and demonstrating potential to significantly reduce screening failure rates from 39.8% to 12.8%.
- The study highlights the importance of using machine learning with semantic analysis as a promising approach for improving patient selection in clinical trials, focusing on key terms related to disease characteristics and laboratory findings
Purpose: To report our experience of intercontinental multidisciplinary oncology videoconferencing between the French mainland and South Pacific to discuss rare and/or complex cancer cases.
Methods: On the first and third Friday of each month, all participants connected between 6:30 am and 8:00 am GMT to discuss using a web conference service.
Results: Between November 2019 and April 2020, 99 cases concerning 78 patients were discussed.
The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity.
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