Deep Learning for Differential Diagnosis and Prediction in EHR Data[go to overview]
Over the last decade, the generation of massive Electronic Health Records (EHR) allowed researchers to explore the secondary use of these data in the field of biomedical informatics researches. Recent researches showed that deep learning models are efficient in collecting important features from EHR data and predict a disease diagnosis. However, these models performed inadequately when it comes to extracting important features from heterogeneous EHR data and predicting multiple disease outcomes. This thesis aims to provide a method to generalize different EHR structures and then train a deep learning model to predict multiple disease outcomes. Thereby, the model would help in differential diagnosis where multiple other disease outcomes are identified given some symptoms. To the best of my knowledge, this is the first time a deep learning model would be used in differential disease diagnosis prediction.
08.07.21 - 10:15
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