PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking

Meng Wang; Jiaheng Zhang; Jun Liu; Wei Hu; Sen Wang; Xue Li; Wenqiang Liu

doi:10.1007/978-3-319-68204-4_23

PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking

Meng Wang
, Jiaheng Zhang
, Jun Liu
, Wei Hu
, Sen Wang
, Xue Li
, Wenqiang Liu

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University
Nanjing University
Griffith University Queensland
University of Queensland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

27 Scopus citations

Abstract

Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

Original language	English
Title of host publication	The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings
Editors	Miriam Fernandez, Claudia d’Amato, Valentina Tamma, Philippe Cudre-Mauroux, Freddy Lecue, Christoph Lange, Juan Sequeda, Jeff Heflin
Publisher	Springer Verlag
Pages	219-227
Number of pages	9
ISBN (Print)	9783319682037
DOIs	https://doi.org/10.1007/978-3-319-68204-4_23
State	Published - 2017
Event	16th International Semantic Web Conference, ISWC 2017 - Vienna, Austria Duration: 21 Oct 2017 → 25 Oct 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10588 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Semantic Web Conference, ISWC 2017
Country/Territory	Austria
City	Vienna
Period	21/10/17 → 25/10/17

Keywords

Disease
Drug
EMR
Linked data
MIMIC-III

Access to Document

10.1007/978-3-319-68204-4_23

Cite this

Wang, M., Zhang, J., Liu, J., Hu, W., Wang, S., Li, X., & Liu, W. (2017). PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking. In M. Fernandez, C. d’Amato, V. Tamma, P. Cudre-Mauroux, F. Lecue, C. Lange, J. Sequeda, & J. Heflin (Eds.), The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings (pp. 219-227). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10588 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68204-4_23

Wang, Meng ; Zhang, Jiaheng ; Liu, Jun et al. / PDD graph : Bridging electronic medical records and biomedical knowledge graphs via entity linking. The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. editor / Miriam Fernandez ; Claudia d’Amato ; Valentina Tamma ; Philippe Cudre-Mauroux ; Freddy Lecue ; Christoph Lange ; Juan Sequeda ; Jeff Heflin. Springer Verlag, 2017. pp. 219-227 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient{\textquoteright}s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.",

keywords = "Disease, Drug, EMR, Linked data, MIMIC-III",

author = "Meng Wang and Jiaheng Zhang and Jun Liu and Wei Hu and Sen Wang and Xue Li and Wenqiang Liu",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th International Semantic Web Conference, ISWC 2017 ; Conference date: 21-10-2017 Through 25-10-2017",

year = "2017",

doi = "10.1007/978-3-319-68204-4\_23",

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isbn = "9783319682037",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "219--227",

editor = "Miriam Fernandez and Claudia d{\textquoteright}Amato and Valentina Tamma and Philippe Cudre-Mauroux and Freddy Lecue and Christoph Lange and Juan Sequeda and Jeff Heflin",

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Wang, M, Zhang, J, Liu, J, Hu, W, Wang, S, Li, X & Liu, W 2017, PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking. in M Fernandez, C d’Amato, V Tamma, P Cudre-Mauroux, F Lecue, C Lange, J Sequeda & J Heflin (eds), The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10588 LNCS, Springer Verlag, pp. 219-227, 16th International Semantic Web Conference, ISWC 2017, Vienna, Austria, 21/10/17. https://doi.org/10.1007/978-3-319-68204-4_23

PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking. / Wang, Meng; Zhang, Jiaheng; Liu, Jun et al.
The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. ed. / Miriam Fernandez; Claudia d’Amato; Valentina Tamma; Philippe Cudre-Mauroux; Freddy Lecue; Christoph Lange; Juan Sequeda; Jeff Heflin. Springer Verlag, 2017. p. 219-227 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10588 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - PDD graph

T2 - 16th International Semantic Web Conference, ISWC 2017

AU - Wang, Meng

AU - Zhang, Jiaheng

AU - Liu, Jun

AU - Hu, Wei

AU - Wang, Sen

AU - Li, Xue

AU - Liu, Wenqiang

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

AB - Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

KW - Disease

KW - Drug

KW - EMR

KW - Linked data

KW - MIMIC-III

UR - https://www.scopus.com/pages/publications/85032196191

U2 - 10.1007/978-3-319-68204-4_23

DO - 10.1007/978-3-319-68204-4_23

M3 - 会议稿件

AN - SCOPUS:85032196191

SN - 9783319682037

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 219

EP - 227

BT - The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings

A2 - Fernandez, Miriam

A2 - d’Amato, Claudia

A2 - Tamma, Valentina

A2 - Cudre-Mauroux, Philippe

A2 - Lecue, Freddy

A2 - Lange, Christoph

A2 - Sequeda, Juan

A2 - Heflin, Jeff

PB - Springer Verlag

Y2 - 21 October 2017 through 25 October 2017

ER -

Wang M, Zhang J, Liu J, Hu W, Wang S, Li X et al. PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking. In Fernandez M, d’Amato C, Tamma V, Cudre-Mauroux P, Lecue F, Lange C, Sequeda J, Heflin J, editors, The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. Springer Verlag. 2017. p. 219-227. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68204-4_23

PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking

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Keywords

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