Details of Research Outputs

TitleReal-world data medical knowledge graph: construction and applications
Author (Name in English or Pinyin)
Li, Linfeng1,2; Wang, Peng3,4; Yan, Jun2; Wang, Yao2; Li, Simin2; Jiang, Jinpeng2; Sun, Zhe2; Tang, Buzhou5; Chang, Tsung-Hui6; Wang, Shenghui1; Liu, Yuting7
Date Issued2020-02-06
Funding Project国家自然科学基金项目
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
Volume Issue Pagesv 103,
[1] Zhao, Z., Han, S., So, I., Architecture of knowledge graph construction techniques. International Journal of Pure and Applied Mathematics 118:19 (2018), 1869–1883.
[2] Barnett, G.O., Cimino, J.J., Hupp, J.A., et al. DXplain. An evolving diagnostic decision-support system. Jama 258:1 (1987), 67–74.
[3] Bisson, L.J., Komm, J.T., Bernas, G.A., et al. Accuracy of a computer-based diagnostic program for ambulatory patients with knee pain. The American journal of sports medicine 42:10 (2014), 2371–2376.
[4] Miller, R.A., Medical diagnostic decision support systems—past, present, and future: a threaded bibliography and brief commentary. Journal of the American Medical Informatics Association 1:1 (1994), 8–27.
[5] Wang M, Liu M, Liu J, et al. Safe medicine recommendation via medical knowledge graph embedding. arXiv preprint arXiv:1710.05980.2017.
[6] Tang, H., Ng, J.H.K., Googling for a diagnosis–use of Google as a diagnostic aid: internet based study. BMJ 333:7579 (2006), 1143–1145.
[7] Gann, B., Giving patients choice and control: health informatics on the patient journey. Yearbook of medical informatics 21:01 (2012), 70–73.
[8] hwe, M.A., Middleton, B., Heckerman, D.E., et al. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. Methods of information in Medicine 30:04 (1991), 241–255.
[9] Kovačević, A., Dehghan, A., Filannino, M., et al. Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives. Journal of the American Medical Informatics Association 20:5 (2013), 859–866.
[10] Tang, B., Cao, H., Wu, Y., et al. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Medical Informatics and Decision Making 13:1 (2013), 1–10.
[11] Luo, L., Yang, Z., Yang, P., et al. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinformatics 34:8 (2017), 1381–1388.
[12] Zhang, Y., Wang, X., Hou, Z., et al. Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods. JMIR medical informatics, 6(4), 2018, e50.
[13] Ji, B., Liu, R., Li, S., et al. A hybrid approach for named entity recognition in Chinese electronic medical record. BMC medical informatics and decision making, 19(2), 2019, 64.
[14] Li, H., Chen, Q., Tang, B., et al. CNN-based ranking for biomedical entity normalization. BMC bioinformatics, 18(11), 2017, 385.
[15] Lou, Y., Zhang, Y., Qian, T., et al. A transition-based joint model for disease named entity recognition and normalization. Bioinformatics 33:15 (2017), 2363–2371.
[16] Li, F., Jin, Y., Liu, W., et al. Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study. JMIR medical informatics, 7(3), 2019, e14830.
[17] Salton, G., Buckley, C., Term-weighting approaches in automatic text retrieval. Information processing & management 24:5 (1988), 513–523.
[18] Vechtomova, O., Robertson, S.E., A domain-independent approach to finding related entities. Information Processing & Management 48:4 (2012), 654–670.
[19] Kang, C., Yin, D., Zhang, R., et al. Learning to rank related entities in Web search. Neurocomputing 166 (2015), 309–318.
[20] Wang, M., Liu, M., Liu, J., et al. Safe medicine recommendation via medical knowledge graph embedding. arXiv preprint arXiv, 2017 1710.05980.
[21] Li, L., Wang, P., Wang, Y., et al. PrTransH: Embedding Probabilistic Medical Knowledge from Real World EMR Data. arXiv preprint arXiv, 2019 1909.00672.
[22] Bordes, A., Usunier, N., Garcia-Duran, A., et al. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 2013, 2787–2795.
[23] Wang, Z., Zhang, J., Feng, J., et al. Knowledge graph embedding by translating on hyperplanes. Twenty-Eighth AAAI conference on artificial intelligence, 2014.
[24] Lin, Y., Liu, Z., Sun, M., et al. Learning entity and relation embeddings for knowledge graph completion. Twenty-ninth AAAI conference on artificial intelligence, 2015.
[25] Finlayson, S.G., LePendu, P., Shah, N.H., Building the graph of medicine from millions of clinical narratives. Scientific data, 1, 2014, 140032.
[26] Rotmensch, M., Halpern, Y., Tlimat, A., et al. Learning a health knowledge graph from electronic medical records. Scientific reports, 7(1), 2017, 5994.
[27] Zhao, C., Jiang, J., Xu, Z., et al. A study of EMR-based medical knowledge network and its applications. Computer methods and programs in biomedicine 143 (2017), 13–23.
[28] Zhao, C., Jiang, J., Guan, Y., et al. EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning. Artificial intelligence in medicine 87 (2018), 49–59.
[29] Shen, Y., Zhang, L., Zhang, J., et al. CBN: Constructing a clinical Bayesian network based on data from the electronic medical record. Journal of biomedical informatics 88 (2018), 1–10.
[30] Bramer, G.R., International statistical classification of diseases and related health problems. Tenth revision. World Health Stat Q 41 (1988), 32–36.
[31] Slee, V.N., The International classification of diseases: ninth revision (ICD-9). Annals of internal medicine 88:3 (1978), 424–426.
[32] Järvelin, K., Kekäläinen, J., Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20:4 (2002), 422–446.
[33] Ester, M., Kriegel, H.-P., Sander, J., et al. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of Second International Conference on Knowledge Discovery and Data Mining, 1996.
[34] Johnson, A.E.W., Pollard, T.J., Shen, L., et al. MIMIC-III, a freely accessible critical care database. Scientific data, 3, 2016, 160035.
Citation statistics
Cited Times:96[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionSchool of Science and Engineering
Corresponding AuthorLiu, Yuting
1.Institute of Information Science, Beijing Jiaotong University, Beijing, China
2.Yidu Cloud Technology Inc., Beijing, China
3.College of Computer Science, Chongqing University, Chongqing, China
4.Southwest Hospital, Chongqing, China
5.Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
6.The School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
7.School of Science, Beijing Jiaotong University, Beijing, China
Recommended Citation
GB/T 7714
Li, Linfeng,Wang, Peng,Yan, Junet al. Real-world data medical knowledge graph: construction and applications[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE,2020.
APA Li, Linfeng., Wang, Peng., Yan, Jun., Wang, Yao., Li, Simin., .. & Liu, Yuting. (2020). Real-world data medical knowledge graph: construction and applications. ARTIFICIAL INTELLIGENCE IN MEDICINE.
MLA Li, Linfeng,et al."Real-world data medical knowledge graph: construction and applications".ARTIFICIAL INTELLIGENCE IN MEDICINE (2020).
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Li, Linfeng]'s Articles
[Wang, Peng]'s Articles
[Yan, Jun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Linfeng]'s Articles
[Wang, Peng]'s Articles
[Yan, Jun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Linfeng]'s Articles
[Wang, Peng]'s Articles
[Yan, Jun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.