Abstract
The healthcare industry is a wealth of biomedical datasets that play a crucial role in the twenty-first century. Each of these datasets has a particular focus, such as diagnosing, preventing, or dealing with diseases while facing pressure to reduce costs and improve efficiency. Nevertheless, many unspecific signs and symptoms can make the doctors’ tasks more difficult and sometimes result in undesirable errors to rank the adequate disease from the patient records and thus affect the quality of services. Therefore, in this chapter, we develop a ranking disease system with enhanced accuracy to solve this problem. For this purpose, we first identify entities from text to specific concepts of interest by extracting semantic annotations with links to concepts of medical ontologies to embed the patient record. Then, we propose a disease–symptom–model view that contains diseases and symptoms as well as their relations. Finally, we rank disease by calculating the similarity between vector representations of medical record of patients and vectors of the diseases identified labels. Our system achieved promising results that demonstrate the effectiveness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
H. Morowitz et al., Models for Biomedical Research: A New Perspective. (National Academy of Sciences, Washington, DC, 1985)
J. Studnicki, D.J. Berndt, J.W. Fisher, Using information systems for public health administration, in Public Health Administration: Principles for Population-Based Management, 2nd edn. (Jones and Bartlett, Sudbury, 2008), pp. 353–380
A. Burgun, O. Bodenreider, Accessing and integrating data and knowledge for biomedical research. Yearb. Med. Inform. 17, 91–101 (2008)
M.G. Weiner, P.J. Embi, Toward reuse of clinical data for research and quality improvement: the end of the beginning? Ann. Intern. Med. 151, 359–360 (2009)
J.C. Maro et al., Design of a national distributed health data network. Ann. Intern. Med. 151, 341–344 (2009)
H.-U. Prokosch, T. Ganslandt, Perspectives for medical informatics. Methods Inf. Med. 48, 38–44 (2009)
R.D. Kush, E. Helton, F.W. Rockhold, C.D. Hardison, Electronic health records, medical research, and the Tower of Babel. N. Engl. J. Med. 358, 1738–1740 (2008)
T. Heath, C. Bizer, Linked data: Evolving the web into a global data space, in Synthesis Lectures on the Semantic Web: Theory and Technology, vol. 1 (2011), pp. 1–136
R. Cyganiak, M. Hausenblas, E. McCuirc, in Linking Government Data (Springer, Berlin, 2011), pp. 135–151
S. Eddamiri, E. Zemmouri, A. Benghabrit, RDF data clustering based on resource and predicate embeddings, in IC3K 2018-Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, vol. 1 (2018), pp. 367–373
P.L. Whetzel et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res. 39, W541–545 (2011)
F. Belleau, M.-A. Nolin, N. Tourigny, P. Rigault, J. Morissette, Bio2RDF: Towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inform. 41, 706–716 (2008)
T. Tudorache, C.I. Nyulas, N.F. Noy, M.A. Musen, Using Semantic Web in ICD-11: Three Years Down the Road in International Semantic Web Conference (2013), pp. 195–211
J. Malone et al., Modeling sample variables with an Experimental Factor Ontology. Bioinformatics 26, 1112–1118 (2010)
U. Consortium, Activities at the universal protein resource (UniProt). Nucleic Acids Res.42, D191–D198 (2014)
D. Croft et al., Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39, D691–D697 (2010)
H.E. Pence, A. Williams, ChemSpider: An Online Chemical Information Resource (2010)
E.L. Willighagen, et al., The ChEMBL database as linked open data. J. Cheminformatics 5, 1–12 (2013)
Kelder, T. et al., WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 40, D1301–D1307 (2012)
P.B. Jensen, L.J. Jensen, S. Brunak, Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13, 395–405 (2012)
E.C. Lau, et al. Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data. Clin. Epidemiol. 3, 259 (2011)
P. Taylor, Personal genomes: when consent gets in the way. Nature 456, 32–33 (2008)
S. Sabra, K.M. Malik, M. Alobaidi, Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives. Comput. Biol. Med. 94, 1–10 (2018)
D.S. Celermajer, C.K. Chow, E. Maijon, N.M. Anstey, K.S. Woo, Cardiovascular disease in the developing world: prevalences, patterns, and the potential of early disease detection. J. Am. Coll. Cardiol. 60, 1207–1216 (2012)
D. Brixner et al., Patient support program increased medication adherence with lower total health care costs despite increased drug spending. J. Manag. Care Spec. Pharm. 25, 770–779 (2019)
C. Phua, V. Lee, K. Smith, R. Gayler, A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119 (2010)
T. Hastie, R. Tibshirani, J. Friedman, in The Elements of Statistical Learning (Springer, Berlin, 2009), pp. 9–41
M. Bundschus, M. Dejori, M. Stetter, V. Tresp, H.-P. Kriegel, Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinf. 9, 207 (2008)
M. Shouman, T. Turner, R. Stocker, Applying k-nearest neighbour in diagnosing heart disease patients. Int. J. Inf. Educ. Technol. 2, 220–223 (2012)
C.-L. Chang, C.-H. Chen, Applying decision tree and neural network to increase quality of dermatologic diagnosis. Expert Syst. Appl. 36, 4035–4041 (2009)
A. Coden et al., Text analysis integration into a medical information retrieval system: challenges related to word sense disambiguation, in Medinfo 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems (2007), p. 2218
M. Poesio, E. Barbu, C. Giuliano, L. Romano, F.B. Kessler, Supervised relation extraction for ontology learning from text based on a cognitively plausible model of relations, in ECAI 2008 3rd Workshop on Ontology Learning and Population (2008), pp. 1–5
E. Mjolsness, D. DeCoste, Machine learning for science: state of the art and future prospects. Science 293, 2051–2055 (2001)
S. Eddamiri, A. Benghabrit, et al., An improved RDF data clustering algorithm. Procedia Comput. Sci. 148, 208–217 (2019)
K.-H. Cheung et al., Extending gene ontology in the context of extracellular RNA and vesicle communication. J. Biomed. Semant. 7, 1–9 (2016)
J. Escudero, J.P. Zajicek, E. Ifeachor, Early detection and characterization of Alzheimer’s disease in clinical scenarios using Bioprofile concepts and K means in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2011), pp. 6470–6473
M. Missikoff, R. Navigli, P. Velardi, Integrated approach to web ontology learning and engineering. Computer 35, 60–63 (2002)
B.J. Liu, W.W. Boonn, Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications in SPIE, vol. 7628 (2010)
W. Zhang, T. Yoshida, X. Tang, A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Syst. Appl. 38, 2758–2765 (2011)
S. Eddamiri, E. Zemmouri, A. Benghabrit, Theme Identification for Linked Medical Data in International Conference on Artificial Intelligence and Industrial Applications (2020), pp. 145–157
Eddamiri, S., Benghabrit, A. and Zemmouri, E. (2020), “RDF graph mining for cluster-based theme identification”, International Journal of Web Information Systems, Vol. 16 No. 2, pp. 223–247. https://doi.org/10.1108/IJWIS-10-2019-0048
B. Steenwinckel et al., in Facilitating the Analysis of Covid-19 Literature Through a Knowledge Graph in International Semantic Web Conference (2020), pp. 344–357
Y. Zhao, G. Karypis, Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach. Learn. 55, 311–331 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Eddamiri, S., Zemmouri, E., Benghabrit, A. (2022). Mining Electronic Health Records of Patients Using Linked Data for Ranking Diseases. In: Ouaissa, M., Boulouard, Z., Ouaissa, M., Guermah, B. (eds) Computational Intelligence in Recent Communication Networks . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77185-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-77185-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77184-3
Online ISBN: 978-3-030-77185-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)