{"id":1707,"date":"2024-10-11T13:17:41","date_gmt":"2024-10-11T11:17:41","guid":{"rendered":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/?p=1707"},"modified":"2024-10-21T16:48:56","modified_gmt":"2024-10-21T14:48:56","slug":"statistical-and-machine-learning-methods-for-survival-data-prediction-performance-assessment-and-interpretability","status":"publish","type":"post","link":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/statistical-and-machine-learning-methods-for-survival-data-prediction-performance-assessment-and-interpretability\/","title":{"rendered":"Statistical and machine learning methods for survival data: prediction, performance assessment and interpretability"},"content":{"rendered":"

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2021<\/em><\/p>\n

PhD\/ DIAI Projects<\/em><\/p>\n

@ED 386 : Sciences Math\u00e9matiques de Paris Centre<\/span><\/p>\n

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PhD student
Ariane CWILING<\/strong>
(MAP5, UPC)<\/p>\n

Supervisors
Olivier BOUAZIZ<\/strong> (MAP5, UPC)
Vittorio PERDUCA<\/strong> (MAP5, UPC)<\/p>\n

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Project Summary<\/strong><\/h3>\n

(Former title: Machine Learning for Survival Data Prediction with applications to Primary Immunodeficiences data)<\/em><\/p>\n

This PhD thesis is at the frontier between mathematics and data science. Its aim is to develop new statistical and machine learning methods for survival analysis with a focus on the prediction of the time to event rather than on the traditional estimation of hazard rates. We build a comprehensive analysis framework for the prediction of the time to event from right-censored data, including a new prediction method as well as new criteria to assess the performance and to enhance the interpretability of such predictors.<\/p>\n

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Le laboratoire<\/a><\/p><\/blockquote>\n