Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Sendong Zhao; Meng Jiang; Bing Qin; Ting Liu; Cheng Xiang 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
, Cheng Xiang Zhai
, Fei Wang

^*Corresponding author for this work

Faculty of Computing

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

7 Scopus citations

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
Pages (from-to)	4468-4483
Number of pages	16
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	34
Issue number	9
DOIs	https://doi.org/10.1109/TKDE.2020.3039469
State	Published - 1 Sep 2022

Keywords

Representation learning
disease prediction
information fusion
network embedding

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10.1109/TKDE.2020.3039469

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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 = "Representation learning, disease prediction, information fusion, network embedding",

author = "Sendong Zhao and Meng Jiang and Bing Qin and Ting Liu and Zhai, \{Cheng Xiang\} and Fei Wang",

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AU - Zhao, Sendong

AU - Jiang, Meng

AU - Qin, Bing

AU - Liu, Ting

AU - Zhai, Cheng Xiang

AU - Wang, Fei

PY - 2022/9/1

Y1 - 2022/9/1

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 - Representation learning

KW - disease prediction

KW - information fusion

KW - network embedding

UR - https://www.scopus.com/pages/publications/85097192268

U2 - 10.1109/TKDE.2020.3039469

DO - 10.1109/TKDE.2020.3039469

M3 - 文章

AN - SCOPUS:85097192268

SN - 1041-4347

VL - 34

SP - 4468

EP - 4483

JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

IS - 9

ER -

Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Abstract

Keywords

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