Abstract
The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit.
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References
Abu-Hanna, A., Cornet, R., De Keizer, N., Crubézy, M., & Tu, S. (2005). protégé as a vehicle for developing medical terminological systems. International Journal of Human Computer Studies, 62(5), 639–663.
Achour, S. L., Dojat, M., Rieux, C., Bierling, P., & Lepage, E. (2001). A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development. Journal of the American Medical Informatics Association, 8(4), 351–360.
Argüello Casteleiro, M., & Des Diz, J. J. (2008). Clinical practice guidelines: A case study of combining OWL-S, OWL, and SWRL. Knowledge-Based Systems, 21(3), 247–255.
Arocha, J. F., Wang, D., & Patel, V. L. (2005). Identifying reasoning strategies in medical decision making: A methodological guide. Journal of Biomedical Informatics, 38(2), 154–171.
Baader, F., Molitor, R., & Tobies S. (1999). Tractable and Decidable Fragments of Conceptual Graphs. Proceedings of the 7th International Conference on Conceptual Structures (ICCS’99), Blacksburg, VA, USA, Tepfenhart, W. & Cyre, W. (Eds.), LNAI 1640, 480-493.
Baget, J.-F., & Mugnier, M.-L. (2002). Extensions of simple conceptual graphs: the complexity of rules and constraints. Journal of Artificial Intelligence Research (JAIR), 16, 425–465.
Balser, M., Reif, W., Schellhorn, G., Stenzel, K., & Thums, A. (2000). Formal system development with KIV, In: Maibaum, T, editor, Fundamental approaches to software engineering, number 1783 in LNCS. New York (USA): Springer; 2000. p. 363–6.
Bell, D. S., Pattison-Gordon, E., & Greenes, R. A. (1994). Experiments in concept modelling for radiographic image reports. Journal of the American Medical Informatics Association, 1, 249–262.
Bernauer, J., & Schoop, D. (1998). Formal classification of medical concept descriptions: graph-oriented operators. Methods of Information in Medicine, 37(4–5), 510–517.
Boaz, D., & Shahar, Y. (2005). A framework for distributed mediation of temporal-abstraction queries to clinical databases. Artificial Intelligence in Medicine, 34(1), 3–24.
Borgida, A. (1996). On the relative expressiveness of description logics and predicate logics. Artificial Intelligence, 82(1–2), 353–367.
Boxwala, A. A., Peleg, M., Tu, S., Ogunyemi, O., Zeng, Q. T., Wang, D., et al. (2004). GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. Journal of Biomedical Informatics, 37(3), 147–161.
Buche, P., Dibie-Barthélemy, J., Hammerlé, O., & Thomopoulos, R. (2006). Fuzzy concepts applied to the design of a database in predictive microbiology. Fuzzy Sets and Systems, 157(9), 1188–1200.
Campbell, K. E., Das, A. K., & Musen, M. A. (1994). A logical foundation for representation of clinical data. Journal of the American Medical Informatics Association, 1(3), 218–232.
Campbell, K. E., Oliver, D. E., & Shortliffe, E. H. (1998). The Unified Medical Language System: toward a collaborative approach for solving terminologic problems. Journal of the American Medical Informatics Association, 5(1), 12–16.
Carloni, O., Leclère, M., & Mugnier, M.-L. (2009). Introducing reasoning into an industrial knowledge management tool. Applied Intelligence, 31(3), 211–224.
Chein, M., & Mugnier, M.-L. (1992). Conceptual graphs: fundamental notions. Artificial Intelligence Review, 6(4), 365–406.
Chein, M. & Mugnier, M-L. (2008). Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs. Series: Advanced Information and Knowledge Processing. Publisher: Springer, 445 pages, Hardcover, ISBN 978-1-84800-285-2. London (United Kingdom).
Chu, S., & Cesnik, B. (2001). Knowledge representation and retrieval using conceptual graphs and free text document self-organisation techniques. International Journal of Medical Informatics, 62(2–3), 121–133.
Corby, O., Dieng-Kuntz, R., Faron-Zucker, C., & Gandon, F. (2006). Searching the semantic web: approximate query processing based on ontologies. IEEE Intelligent Systems Journal, 21(1), 20–27.
Dau, F., & Eklund, P. (2008). A diagrammatic reasoning system for the description logic. Journal of Visual Languages and Computing, 19(5), 539–573.
De Clercq, P. A., Blom, J. A., Korsten, H. H. M., & Hasman, A. (2004). Approaches for creating computer-interpretable guidelines that facilitate decision support. Artificial Intelligence in Medicine, 31(1), 1–27.
Delamarre, D., Burgun, A., Seka, L. P., & Le Beux, P. (1995). Automated coding of patient discharge summaries using conceptual graphs. Methods of Information in Medicine, 34(4), 345–351.
Delugach, H.S., & Rochowiak, D. (2008). Grounded Conceptual Graph Models. Proceedings of the 16th International Conference on Conceptual Structures (ICCS 2008), Eklund P. & Haemmerlé O. (Eds.), Conceptual Structures: Knowledge Visualization and Reasoning, Toulouse (France), LNAI 5113, pp. 269–281.
Dibie-Barthélemy, J., Haemmerlé, O., & Salvat, E. (2006). A semantic validation of conceptual graphs. Knowledge-Based Systems, 19(7), 498–510.
Dieng-Kuntz, R., & Corby, O. (2005). Conceptual Graphs for Semantic Web Applications. Proceedings of the 13th Int.Conference on Conceptual Structures (ICCS’2005), Dau, F., Mugnier, M-L., Stumme, G. (editors), Kassel (Germany), July 17-23, 2005, Springer-Verlag, LNAI 3596, ISBN 978-3-540-27783-5, p. 19-50.
Dieng-Kuntz, R., Minier, D., Ruzicka, M., Corby, F., Corby, O., & Alamarguy, L. (2006). Building and using a medical ontology for knowledge management and cooperative work in a health care Network. Computers in Biology and Medicine, 36(7–8), 871–892.
Dojat, M., Ramaux, N., & Fontaine, D. (1998). Scenario recognition for temporal reasoning in medical domains. Artificial Intelligence in Medicine, 14(1–2), 139–155.
Dojat, M., Pachet, F., Guessoum, Z., Touchard, D., Harf, A., & Brochard, L. (1997). NeoGanesh: A working system for the automated control of assisted ventilation in ICUs. Artificial Intelligence in Medicine, 11(2), 97–117.
Duftschmid, G., Miksch, S., & Gall, W. (2002). Verification of temporal scheduling constraints in clinical practice guidelines. Artificial Intelligence in Medicine, 25(2), 93–121.
Fürst, F., Leclère, M., & Trichet, F. (2003). Ontological engineering and mathematical knowledge management: A formalization of projective geometry. Annals of Mathematics and Artificial Intelligence, 38(1), 65–89.
Gardner, M.-R. (2004). Computerized clinical decision-support in respiratory care. Respiratory Care Journal., 49(4), 378–386.
Genest, D., & Chein, M. (2005). A content-search information retrieval process based on conceptual graphs. Knowledge And Information Systems, 8(3), 292–309.
GraphIK Team (2012). CoGUI (Conceptual Graphs Graphical User Interface), User guide, document available at the following web site: http://www2.lirmm.fr/cogui/index.php. Last accessed May 28, 2012.
Henry, S. B., & Mead, C. N. (1997). Nursing classification systems: necessary but not sufficient for representing “what nurses do” for inclusion in computer-based patient record systems. Journal of the American Medical Informatics Association, 4(3), 222–232.
Hommersom, A., Groot, P., Lucas, P. J. F., Balser, M., & Schmitt, J. (2007). Verification of medical guidelines using background knowledge in task networks. IEEE Transactions on Knowledge and Data Engineering, 19(6), 832–846.
Horrocks, I., Patel-Schneider, P. F., Bechhofer, S., & Tsarkov, D. (2005). OWL rules: A proposal and prototype implementation. Web Semantics: Science, Services and Agents on the World Wide Web, 3(1), 23–40.
Hripcsak, G., Ludemann, P., Pryor, T. A., Wigertz, O. B., & Clayton, P. D. (1994). Rationale for the Arden Syntax. Computers and Biomedical Research, 27(4), 291–324.
Kamsu Foguem, B., Coudert, T., Geneste, L., & Beler, C. (2008). Knowledge formalization in experience feedback processes: an ontology-based approach. Computers in Industry, 59(7), 694–710.
Kamsu-Foguem, B., & Chapurlat, V. (2006). Requirements modelling and formal analysis using graph operations. International Journal of Production Research, 44(17), 3451–3470.
Khelif, K., Dieng-Kuntz, R., & Barbry, P. (2007). An ontology-based approach to support text mining and information retrieval in the biological domain. Journal of Universal Computer Science, 13(12), 1881–1907.
Kitabchi, A. E., & Nyenwe, E. A. (2006). Hyperglycemic crises in diabetes mellitus: diabetic ketoacidosis and hyperglycemic hyperosmolar state. Endocrinology And Metabolism Clinics Of North America, 35(4), 725–751.
Latoszek-Berendsen, A., Tange, H., van den Herik, H.J., & Hasman, A. (2010). From Clinical Practice Guidelines to Computer-interpretable Guidelines. A Literature Overview. Methods of Information in Medicine. 18;49(6):550-570.
MacIsaac, R. J., Lee, L. Y., McNeil, K. J., Tsalamandris, C., & Jerums, G. (2002). Influence of age on the presentation and outcome of acidotic and hyperosmolar diabetic emergencies. Internal Medicine Journal, 32(8), 379–385.
Magee, M. F., & Bhatt, B. A. (2001). Management of decompensated diabetes. Diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome. Critical Care Clinics, 17(1), 75–106.
Manjarrés Riesco, A., Martínez Tomás, R., & Mira Mira, J. (2000). A customisable framework for the assessment of therapies in the solution of therapy decision tasks. Artificial Intelligence in Medicine, 18(1), 57–82.
Marino, P.L., & Sutin, K.M. The ICU Book. ISBN-10: 078174802X, ISBN-13: 978-0781748025. Publisher: Lippincott Williams and Wilkins; 3rd Revised edition edition (1 Oct 2006), Philadelphia, Pennsylvania, U.S.A. Paperback: 1065 pages.
Moulin, B. (1997). Temporal Contexts for Discourse Representation: An Extension of the Conceptual Graph Approach. Applied Intelligence, 7(3), 227–255.
Mugnier, M. L. (1995). On generalization/specialization for conceptual graphs. Journal of Experimental and Theoretical Artificial Intelligence, 7(3), 325–344.
Mugnier, M.-L., & Leclère, M. (2007). On querying simple conceptual graphs with negation. Data & Knowledge Engineering, 60(3), 468–493.
Müller, R (1997). The CliniCon Framework for Context Representation in Electronic Patient Records. In: Daniel R. Masys (ed.): Proceedings of the 1997 Fall Symposium of the American Medical Informatics Association (formerly SCAMC), Nashville, TN, USA. Hanley & Belfus, pp. 178-182.
Musen, M., Tu, S., Das, A., & Shahar, Y. (1996). EON: A component-based approach to automation of protocol-directed therapy. Journal of the American Medical Informatics Association, 3(6), 367–388.
Naudet, Y., Latour, T., Guedria, W., & Chen, D. (2010). Towards a systemic formalisation of interoperability. Computers in Industry, 61(2), 176–185.
Park, S., & Kyu Lee, J. (2007). Rule identification using ontology while acquiring rules from Web pages. International Journal of Human Computer Studies, 65(7), 659–673.
Patel, V. L., Arocha, J. F., & Kaufman, D. R. (1994). Diagnostic reasoning and medical expertise. In D. Medin (Ed.), The psychology of learning and motivation, 31 (pp. 187–252). San Diego: Academic Press.
Patel, V. L., Arocha, J. F., Diermeier, M., How, J., & Mottur-Pilson, C. (2001). Cognitive psychological studies of representation and use of clinical practice guidelines. International Journal of Medical Informatics, 63(3), 147–167.
Peleg, M., Tu, S., Bury, J., Ciccarese, P., Fox, J., Greenes, R. A., et al. (2003). Comparing computer-interpretable guideline models: a case-study approach. Journal of the American Medical Informatics Association, 10(1), 52–68.
Pérez, B., & Porres, I. (2010). Authoring and verification of clinical guidelines: A model driven approach. Journal of Biomedical Informatics, 43(4), 520–536.
Purves, N. (1998). PRODIGY: implementing clinical guidance using computers. British Journal of General Practice, 48(434), 1552–1553.
Quaglini, S., Stefanelli, M., Cavallini, A., Micieli, G., Fassino, C., & Mossa, C. (2000). Guideline-based careflow systems. Artificial Intelligence in Medicine, 20(1), 5–22.
Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1–2), 79–133.
Shahar, Y. (2000). Dimension of time in illness: an objective view. Annals of Internal Medicine, 132(1), 45–53.
Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267–298.
Shahar, Y., Miksch, S., & Johnson, P. (1998). The asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine, 14(1–2), 29–51.
Shahar, Y., Young, O., Shalom, E., Galperin, M., Mayaffit, A., Moskovitch, R., et al. (2004). A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools. Journal of Biomedical Informatics, 37(5), 325–344.
Slaughter, L. A., Soergel, D., & Rindflesch, T. C. (2006). Semantic representation of consumer questions and physician answers. International Journal of Medical Informatics, 75(7), 513–529.
Solnon, C. (2010). AllDifferent-based filtering for subgraph isomorphism. Artificial Intelligence, 174(12–13), 850–864.
Sowa, J. F. (1984). Conceptual structures: information processing in mind and machine. The Systems Programming Series (Hardcover) (p. 481). Boston: Addison-Wesley Longman Publishing Co., Inc.
Sowa, J. F. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations (p. 608). Pacific Grove: Brooks Cole Publishing Co. ISBN 0-534-94965-7.
Sowa, J. F., & Zachman, J. A. (1992). Extending and formalizing the framework for information systems architecture. IBM Systems Journal, 31(3), 590–616.
Steimann, F., & Adlassnig, K.-P. (1998). Fuzzy Medical Diagnosis. In E. Ruspini, P. Bonissone, & W. Pedrycz (Eds.), Handbook of Fuzzy Computation (pp. G13.1:1–G13.1:14). Bristol: IOP Publishing Ltd and Oxford University Press.
Stoner, G. D. (2005). Hyperosmolar hyperglycemic state. American Family Physician, 71(9), 1723–1730.
Sutton, D. R., Taylor, P., & Earle, K. (2006). Evaluation of PROforma as a language for implementing medical guidelines in a practical context. BMC Medical Informatics and Decision Making, 6, 20.
Takrouri, M. S. M. (2004). Intensive Care Unit. The Internet Journal of Health, 3(2). doi:10.5580/1c97.
Ten Teije, A., Marcos, M., Balser, M., Van Croonenborg, J., Duelli, C., Van Harmelen, F., et al. (2006). Improving medical protocols by formal methods. Artificial Intelligence in Medicine, 36(3), 193–209.
Terenziani, P., Montani, S., Bottrighi, A., Torchio, M., Molino, G., & Correndo, G. (2004). The GLARE approach to clinical guidelines: main features. Studies In Health Technology And Informatics, 101(3), 162–166.
Thomopoulos, R., Buche, P., & Hammerlé, O. (2003). Representation of weakly structured imprecise data for fuzzy querying. Fuzzy Sets and Systems, 140(1), 111–128.
Tu, S. W., Campbell, J. R., Glasgow, J., Nyman, M. A., McClure, R., McClay, J., et al. (2007). The SAGE guideline model: achievements and overview. Journal of the American Medical Informatics Association, 14(5), 589–598.
Volot, F., Joubert, M., & Fieschi, M. (1998). Review of biomedical knowledge and data representation with conceptual graphs. Methods of Information in Medicine, 37(1), 86–96.
Wang, D., Peleg, M., Tu, S. W., Boxwala, A. A., Greenes, R. A., Patel, V. L., et al. (2002). Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: A literature review of guideline representation models. International Journal of Medical Informatics, 68(1–3), 59–70.
Ward, N. S. (2004). Using computers for intensive care unit research. Respiratory Care Journal., 49(5), 518–522.
Yao, H., & Etzkorn, L. (2006). Automated conversion between different knowledge representation formats. Knowledge-Based Systems, 19(6), 404–412.
Zweigenbaum, P. (1994). MENELAS: an access system for medical records using natural language. Computer Methods and Programs in Biomedicine, 45(1–2), 117–120.
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Kamsu-Foguem, B., Tchuenté-Foguem, G. & Foguem, C. Using conceptual graphs for clinical guidelines representation and knowledge visualization. Inf Syst Front 16, 571–589 (2014). https://doi.org/10.1007/s10796-012-9360-2
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DOI: https://doi.org/10.1007/s10796-012-9360-2