Injecting Domain Knowledge in Electronic Medical Records to Improve Hospitalization Prediction

Abstract : Electronic medical records (EMR) contain key information about the different symptomatic episodes that a patient went through. They carry a great potential in order to improve the well-being of patients and therefore represent a very valuable input for artificial intelligence approaches. However, the explicit knowledge directly available through these records remains limited, the extracted features to be used by machine learning algorithms do not contain all the implicit knowledge of medical expert. In order to evaluate the impact of domain knowledge when processing EMRs, we augment the features extracted from EMRs with ontological resources before turning them into vectors used by machine learning algorithms. We evaluate these augmentations with several machine learning algorithms to predict hospitalization. Our approach was experimented on data from the PRIMEGE PACA database that contains more than 350,000 consultations carried out by 16 general practitioners (GPs).
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Communication dans un congrès
ESWC 2019 - The 16th European Semantic Web Conference, Jun 2019, Portorož, Slovenia. Springer’s Lecture Notes in Computer Science (LNCS)
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https://hal.archives-ouvertes.fr/hal-02064421
Contributeur : Raphaël Gazzotti <>
Soumis le : jeudi 14 mars 2019 - 22:33:02
Dernière modification le : vendredi 22 mars 2019 - 13:52:28

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  • HAL Id : hal-02064421, version 2

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Raphaël Gazzotti, Catherine Faron Zucker, Fabien Gandon, Virginie Lacroix-Hugues, David Darmon. Injecting Domain Knowledge in Electronic Medical Records to Improve Hospitalization Prediction. ESWC 2019 - The 16th European Semantic Web Conference, Jun 2019, Portorož, Slovenia. Springer’s Lecture Notes in Computer Science (LNCS). 〈hal-02064421v2〉

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