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Decision making strategy for antenatal echographic screening of foetal abnormalities using statistical learning

Abstract : In this thesis, we propose a method to build a decision support tool for the diagnosis of rare diseases. We aim to minimize the number of medical tests necessary to achieve a state where the uncertainty regarding the patient's disease is less than a predetermined threshold. In doing so, we take into account the need in many medical applications, to avoid as much as possible, any misdiagnosis. To solve this optimization task, we investigate several reinforcement learning algorithm and make them operable in our high-dimensional. To do this, we break down the initial problem into several sub-problems and show that it is possible to take advantage of the intersections between these sub-tasks to accelerate the learning phase. The strategies learned are much more effective than classic greedy strategies. We also present a way to combine expert knowledge, expressed as conditional probabilities, with clinical data. This is crucial because the scarcity of data in the field of rare diseases prevents any approach based solely on clinical data. We show, both empirically and theoretically, that our proposed estimator is always more efficient than the best of the two models (expert or data) within a constant. Finally, we show that it is possible to effectively integrate reasoning taking into account the level of granularity of the symptoms reported while remaining within the probabilistic framework developed throughout this work.
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Submitted on : Monday, November 25, 2019 - 3:58:08 PM
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Rémi Besson. Decision making strategy for antenatal echographic screening of foetal abnormalities using statistical learning. Applications [stat.AP]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLX037⟩. ⟨tel-02379295⟩

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