research-article

MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data

Authors:

Kai LeiAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 719 - 728

https://doi.org/10.1145/3357384.3357934

Published: 03 November 2019 Publication History

Abstract

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios.

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Cited By

Wang DWang XAbedin MWang SYin Y(2023)Interpretable multi-hop knowledge reasoning for gastrointestinal diseaseAnnals of Operations Research10.1007/s10479-023-05650-6Online publication date: 30-Oct-2023
https://doi.org/10.1007/s10479-023-05650-6
Azam MNaeem S(2022)Academic integrity among medical students and postgraduate trainees in the teaching hospitals of South Punjab PakistanHealth Information & Libraries Journal10.1111/hir.1245839:4(377-384)Online publication date: 14-Oct-2022
https://doi.org/10.1111/hir.12458
Jeon SShin CKo EMoon J(2021)A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed SettingApplied Sciences10.3390/app1119907211:19(9072)Online publication date: 29-Sep-2021
https://doi.org/10.3390/app11199072
Show More Cited By

Index Terms

MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Crowdsourcing
        Trust
    2. Web mining
      1. Data extraction and integration

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cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 ACM.

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Publication History

Published: 03 November 2019

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Shenzhen Fundamental Research Project
National Natural Science Foundation of China
Shenzhen Project
PCL Future Regional Network Facilities for Large-scale Experiments and Applications

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CIKM '19

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

Wang DWang XAbedin MWang SYin Y(2023)Interpretable multi-hop knowledge reasoning for gastrointestinal diseaseAnnals of Operations Research10.1007/s10479-023-05650-6Online publication date: 30-Oct-2023
https://doi.org/10.1007/s10479-023-05650-6
Azam MNaeem S(2022)Academic integrity among medical students and postgraduate trainees in the teaching hospitals of South Punjab PakistanHealth Information & Libraries Journal10.1111/hir.1245839:4(377-384)Online publication date: 14-Oct-2022
https://doi.org/10.1111/hir.12458
Jeon SShin CKo EMoon J(2021)A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed SettingApplied Sciences10.3390/app1119907211:19(9072)Online publication date: 29-Sep-2021
https://doi.org/10.3390/app11199072
Callahan TTripodi IPielke-Lombardo HHunter L(2020)Knowledge-Based Biomedical Data ScienceAnnual Review of Biomedical Data Science10.1146/annurev-biodatasci-010820-0916273:1(23-41)Online publication date: 20-Jul-2020
https://doi.org/10.1146/annurev-biodatasci-010820-091627
Liu MZhang JLin X(2020)Design and Implementation of Medical Process Visualization CDSS Oriented to NCCN GuidelinesProceedings of the 2020 International Conference on Internet Computing for Science and Engineering10.1145/3424311.3424322(30-34)Online publication date: 14-Jan-2020
https://dl.acm.org/doi/10.1145/3424311.3424322
Wu JUl Hoque MReiske GWeigle MBradshaw BGaff HLi JKwan CHuang RWu DMarchionini GHe DCunningham SHansen P(2020)A Comparative Study of Sequence Tagging Methods for Domain Knowledge Entity Recognition in Biomedical PapersProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 202010.1145/3383583.3398602(397-400)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1145/3383583.3398602
Dumitriu AMolony CDaluwatte C(2020)Graph-Based Natural Language Processing for the Pharmaceutical IndustryProvenance in Data Science10.1007/978-3-030-67681-0_6(75-110)Online publication date: 28-Dec-2020
https://doi.org/10.1007/978-3-030-67681-0_6

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