MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data

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...

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Main Authors: DENG, Yang, LI, Yaliang, SHEN, Ying, DU, Nan, FAN, Wei, YANG, Min, LEI, Kai
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/9107
https://ink.library.smu.edu.sg/context/sis_research/article/10110/viewcontent/3357384.3357934.pdf
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spelling sg-smu-ink.sis_research-101102024-08-01T14:54:20Z MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data DENG, Yang LI, Yaliang SHEN, Ying DU, Nan FAN, Wei YANG, Min LEI, Kai 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 decisionmaking 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 realworld 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. 2019-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9107 info:doi/10.1145/3357384.3357934 https://ink.library.smu.edu.sg/context/sis_research/article/10110/viewcontent/3357384.3357934.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Information systems Trust Data extraction and integration Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Information systems
Trust
Data extraction and integration
Databases and Information Systems
spellingShingle Information systems
Trust
Data extraction and integration
Databases and Information Systems
DENG, Yang
LI, Yaliang
SHEN, Ying
DU, Nan
FAN, Wei
YANG, Min
LEI, Kai
MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data
description 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 decisionmaking 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 realworld 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.
format text
author DENG, Yang
LI, Yaliang
SHEN, Ying
DU, Nan
FAN, Wei
YANG, Min
LEI, Kai
author_facet DENG, Yang
LI, Yaliang
SHEN, Ying
DU, Nan
FAN, Wei
YANG, Min
LEI, Kai
author_sort DENG, Yang
title MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data
title_short MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data
title_full MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data
title_fullStr MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data
title_full_unstemmed MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data
title_sort medtruth: a semi-supervised approach to discovering knowledge condition information from multi-source medical data
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/9107
https://ink.library.smu.edu.sg/context/sis_research/article/10110/viewcontent/3357384.3357934.pdf
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