Interpretable disease prediction using heterogeneous patient records with self-attentive fusion encoder | Zendy

Heeyoung Kwak | Zendy; Jooyoung Chang | Zendy; Byeongjin Choe | Zendy; Sang Min Park | Zendy; Kyomin Jung | Zendy

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Interpretable disease prediction using heterogeneous patient records with self-attentive fusion encoder

Author(s) -

Heeyoung Kwak,

Jooyoung Chang,

Byeongjin Choe,

Sang Min Park,

Kyomin Jung

Publication year - 2021

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocab109

Subject(s) - computer science , artificial intelligence , concatenation (mathematics) , machine learning , medical record , health records , inference , recurrent neural network , population , autoencoder , sample (material) , disease , deep learning , artificial neural network , data mining , health care , medicine , mathematics , chemistry , environmental health , chromatography , combinatorics , pathology , economics , radiology , economic growth

We propose an interpretable disease prediction model that efficiently fuses multiple types of patient records using a self-attentive fusion encoder. We assessed the model performance in predicting cardiovascular disease events, given the records of a general patient population.

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