Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Sendong Zhao; Meng Jiang; Bing Qin; Ting Liu; Chengxiang Zhai; Fei Wang

doi:10.1109/TKDE.2020.3039469

Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Sendong Zhao, Meng Jiang, Bing Qin, Ting Liu, Chengxiang Zhai, Fei Wang

Research output: Contribution to journal › Article › peer-review

Abstract

Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.

Original language	English (US)
Journal	IEEE Transactions on Knowledge and Data Engineering
DOIs	https://doi.org/10.1109/TKDE.2020.3039469
State	Accepted/In press - 2020

Keywords

Computer science
Disease Prediction
Diseases
Encoding
Information Fusion
Learning systems
Medical diagnosis
Medical diagnostic imaging
Network Embedding
Representation Learning
Semantics

ASJC Scopus subject areas

Information Systems
Computer Science Applications
Computational Theory and Mathematics

Online availability

10.1109/TKDE.2020.3039469

Cite this

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title = "Structural and Textual Information Fusion for Symptom and Disease Representation Learning",

abstract = "Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.",

keywords = "Computer science, Disease Prediction, Diseases, Encoding, Information Fusion, Learning systems, Medical diagnosis, Medical diagnostic imaging, Network Embedding, Representation Learning, Semantics",

author = "Sendong Zhao and Meng Jiang and Bing Qin and Ting Liu and Chengxiang Zhai and Fei Wang",

note = "Publisher Copyright: IEEE",

year = "2020",

doi = "10.1109/TKDE.2020.3039469",

language = "English (US)",

journal = "IEEE Transactions on Knowledge and Data Engineering",

issn = "1041-4347",

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T1 - Structural and Textual Information Fusion for Symptom and Disease Representation Learning

AU - Zhao, Sendong

AU - Jiang, Meng

AU - Qin, Bing

AU - Liu, Ting

AU - Zhai, Chengxiang

AU - Wang, Fei

N1 - Publisher Copyright: IEEE

PY - 2020

Y1 - 2020

N2 - Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.

AB - Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.

KW - Computer science

KW - Disease Prediction

KW - Diseases

KW - Encoding

KW - Information Fusion

KW - Learning systems

KW - Medical diagnosis

KW - Medical diagnostic imaging

KW - Network Embedding

KW - Representation Learning

KW - Semantics

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DO - 10.1109/TKDE.2020.3039469

M3 - Article

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JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

SN - 1041-4347

ER -

Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Abstract

Keywords

ASJC Scopus subject areas

Online availability

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