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Journal of Intelligent Information Systems
Erschienen in: Journal of Intelligent Information Systems 2/2020

24.07.2020

Finding discriminatory features from electronic health records for depression prediction

verfasst von: Liang Kuang Tai, Winny Setyonugroho, Arbee L. P. Chen

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2020

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Abstract

Depression, a common mental disorder, affects not only individuals but also families and society. In the beginning stage, most of the depressive people do not know they are suffering from depression. Some of them visit different medical departments to ask for help. However, their symptoms may not be relieved because of not having a proper diagnosis. In this paper, we find discriminatory features for establishing an early depression detection model by analyzing medical data. These features are composed of patients’ medical information, including diagnosed diseases and medical departments. We use real-world electronic health records dataset from the Taiwan National Health Insurance Research Database for the analysis and focus on young people aged 10-24 years. The experiment results show that our model can detect future diagnosis of depression based on patients’ records up to 90 days in advance. Furthermore, even better performance can be achieved with longer observation time.
Vorheriger Artikel Healthcare predictive analytics for disease progression: a longitudinal data fusion approach

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Metadaten
Titel
Finding discriminatory features from electronic health records for depression prediction
verfasst von
Liang Kuang Tai
Winny Setyonugroho
Arbee L. P. Chen
Publikationsdatum
24.07.2020
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 2/2020
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-020-00611-y

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