Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-24T18:50:13.864Z Has data issue: false hasContentIssue false
  • English
  • Français

Checking the quality of clinical guidelines using automated reasoning tools

Published online by Cambridge University Press:  01 November 2008

ARJEN HOMMERSOM ,
PETER J. F. LUCAS  and
PATRICK VAN BOMMEL
ARJEN HOMMERSOM
Affiliation:
Section on Model-based System Development, Institute for Computing and Information Sciences, Radboud University Nijmegen, PO Box 9010, 6500 GL Nijmegen, The Netherlands (e-mail: arjenh@cs.ru.nl, peterl@cs.ru.nl, pvb@cs.ru.nl)
PETER J. F. LUCAS
Affiliation:
Section on Model-based System Development, Institute for Computing and Information Sciences, Radboud University Nijmegen, PO Box 9010, 6500 GL Nijmegen, The Netherlands (e-mail: arjenh@cs.ru.nl, peterl@cs.ru.nl, pvb@cs.ru.nl)
PATRICK VAN BOMMEL
Affiliation:
Section on Model-based System Development, Institute for Computing and Information Sciences, Radboud University Nijmegen, PO Box 9010, 6500 GL Nijmegen, The Netherlands (e-mail: arjenh@cs.ru.nl, peterl@cs.ru.nl, pvb@cs.ru.nl)
Get access Rights & Permissions [Opens in a new window]

Abstract

Requirements about the quality of clinical guidelines can be represented by schemata borrowed from the theory of abductive diagnosis, using temporal logic to model the time-oriented aspects expressed in a guideline. Previously, we have shown that these requirements can be verified using interactive theorem proving techniques. In this paper, we investigate how this approach can be mapped to the facilities of a resolution-based theorem prover, otter and a complementary program that searches for finite models of first-order statements, mace-2. It is shown that the reasoning required for checking the quality of a guideline can be mapped to such a fully automated theorem-proving facilities. The medical quality of an actual guideline concerning diabetes mellitus 2 is investigated in this way.

Keywords

automated reasoningclinical guidelinetemporal logicabduction
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 8 , Issue 5-6 , November 2008 , pp. 611 - 641
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, J. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26, 11, 832–843.CrossRefGoogle Scholar
Areces, C., Gennari, R., Heguiabehere, J. and De Rijke, M. 2000. Tree-based heuristics in modal theorem proving. In Proc. of the ECAI'2000. Berlin, Germany.Google Scholar
Bäumler, S., Balser, M., Dunets, A., Reif, W. and Schmitt, J. 2006. Verification of medical guidelines by model checking – a case study. In Proc. of 13th International SPIN Workshop on Model Checking of Software, Valmari, A., Ed. LNCS, vol. 3925. Springer-Verlag, Heidelberg, 219–233.Google Scholar
Clark, K. L. 1978. Negation as failure. In Logic and Data Bases, Gaillaire, H. and Minker, J., Eds. Plenum Press, New York, 293–322.CrossRefGoogle Scholar
Console, L., Dupre, D. T. and Torasso, P. 1991. On the relationship between abduction and deduction. Journal of Logic and Computation 1 (5), 661–690.CrossRefGoogle Scholar
Console, L. and Torasso, P. 1991. A spectrum of logical definitions of model-based diagnosis. Computational Intelligence 7 (3), 133–141.CrossRefGoogle Scholar
Davis, M., Logemann, G. and Loveland, D. 1962. A machine program for theorem proving. Communications of the ACM 5 (7), 394–397.CrossRefGoogle Scholar
Davis, M. and Putman, H. 1969. A computing procedure for quantification theory. Journal of the ACM 7, 201–215.CrossRefGoogle Scholar
Duftschmid, G., Miksch, S. and Gall, W. 2002. Verification of temporal scheduling constraints in clinical practice guidelines. Artificial Intelligence in Medicine 25, 93–121.CrossRefGoogle ScholarPubMed
Evans, J. S. 1988. The knowledge elicitation problem: A psychological perspective. Behaviour and Information Technology 7 (2), 111–130.CrossRefGoogle Scholar
Fox, J. and Das, S. 2000. Safe and Sound: Artificial Intelligence in Hazardous Applications, MIT Press, Cambridge, MA, USA.Google Scholar
Fox, J., Patkar, V. and Thomson, R. 2006. Decision support for health care: The proforma evidence base. Informatics in Primary Care 14 (1), 49–54.Google ScholarPubMed
Gabbay, D. M. 1989. The declarative past and imperative future: Executable temporal logic for interactive systems. In Temporal Logic in Specification, Vol. 398, Barringer, H., Ed., LNCS. Springer-Verlag, Berlin, 409–448.CrossRefGoogle Scholar
Ganong, W. 2005. Review of Medical Physiology, 22nd ed.McGraw-Hill, London, UK.Google Scholar
Groot, P., Hommersom, A., Lucas, P. J. F., Serban, R., Ten Teije, A. and van Harmelen, F. 2007. The role of model checking in critiquing based on clinical guidelines. In Proc. of AIME-2007. Springer-Verlag, Heidelberg, 411–420.Google Scholar
Guyton, A. and Hall, J. 2000. Textbook of Medical Physiology. W. B. Saunders Company, Philadelphia, PA, USA.Google Scholar
Hommersom, A. J., Groot, P., Lucas, P. J. F., Balser, M. and Schmitt, J. 2007. Verification of medical guidelines using background knowledge in task networks. IEEE Transactions on Knowledge and Data Engineering 19 (6), 832–846.CrossRefGoogle Scholar
Jech, T. 1995. OTTER experiments in a system of combinatory logic. Journal of Automated Reasoning 14 (3), 413–426.CrossRefGoogle Scholar
Kersting, K. and De Raedt, L. 2000. Bayesian logic programs. In Proc. of the Work-in-Progress Track at the 10th International Conference on Inductive Logic Programming, Cussens, J. and Frisch, A., Eds. Vol. 35 of CEUR workshop proceedings, Aachen Germany, 138–155.Google Scholar
Knuth, D. E. and Bendix, P. B. 1970. Simple word problems in universal algebras. In Computational Algebra, Leech, J., Ed. Pergamon, 263–297.Google Scholar
Kosara, R. and Miksch, S. May 2001. Metaphors of movement: A visualization and user interface for time-oriented, skeletal plans. Artificial Intelligence in Medicine 22 (2), 111–131.CrossRefGoogle ScholarPubMed
Lehmann, E. 1998. Compartmental models for glycaemic prediction and decision-support in clinical diabetes care: Promise and reality. Computer Methods and Programs in Biomedicine 56 (2), 193–204.CrossRefGoogle Scholar
Lucas, P. J. F. 1993. The representation of medical reasoning models in resolution-based theorem provers. Artificial Intelligence in Medicine 5, 395–419.CrossRefGoogle ScholarPubMed
Lucas, P. J. F. 1995. Logic engineering in medicine. The Knowledge Engineering Review 10 (2)153–179.CrossRefGoogle Scholar
Lucas, P. J. F. 1997. Symbolic diagnosis and its formalisation. The Knowledge Engineering Review 12, 2, 109–146.CrossRefGoogle Scholar
Lucas, P. J. F. 2003. Quality checking of medical guidelines through logical abduction. In Proc. of AI-2003 (Research and Developments in Intelligent Systems XX), Coenen, F., Preece, A. and Mackintosh, A., Eds. Springer, London, 309–321.Google Scholar
Lucas, P. J. F. and van Der Gaag, L. C. 1991. Principles of Intelligent Systems. Addison-Wesley, Wokingham.Google Scholar
Magni, P., Bellazzi, R., Sparacino, G. and Cobelli, C. 2000. Bayesian identification of a population compartmental model of c-peptide kinetics. Annals of Biomedical Engineering 28, 812–823.CrossRefGoogle ScholarPubMed
Marcos, M., Balser, M., Ten Teije, A. and van Harmelen, F. 2002. From informal knowledge to formal logic: A realistic case study in medical protocols. In Proc. of the 12th EKAW-2002, Sigüenza, Spain.Google Scholar
Markey, N. 2003. Temporal logic with past is exponentially more succinct. EATCS Bulletin 79, 122–128.Google Scholar
McCune, W. June, 2001. MACE 2.0 Reference Manual and Guide. Tech. Memo ANL/MCS-TM-249, Argonne National Laboratory, Argonne, IL.CrossRefGoogle Scholar
McCune, W. August 2003. Otter 3.3 Reference Manual. Tech. Memo ANL/MCS-TM-263, Argonne National Laboratory, Argonne, IL.Google Scholar
Miller, P. 1984. A Critiquing Approach to Expert Computer Advice: ATTENDING. Pittman, London.Google Scholar
Moore, R. C. 1979. Reasoning about Knowledge and Action. Ph.D. Thesis, MIT.Google Scholar
Moszkowski, B. 1985. A temporal logic for multilevel reasoning about hardware. In IEEE Computer, 2nd ed., Vol. 18, Los Alamitos, CA, USA, 10–19.Google Scholar
Ohlbach, H. J. 1988. A resolution calculus for modal logics. In Proc. CADE-88: International Conference on Automated Deduction, Lusk, E. and Overbeek, R., Eds. LNCS, vol. 310. Springer-Verlag, Heidelberg, 500–516.CrossRefGoogle Scholar
Patil, R. 1981. Causal representation of patient illness for ELECTROLYTE and ACID-BASE diagnosis. Tech. Rep. MIT/LCS/TR-267, MIT.Google Scholar
Peleg, M., Boxwala, A., Ogunyemi, O., Zeng, P., Tu, S., Lacson, R., Begnstam, E. and Ash, N. 2000. GLIF3: The evolution of a guideline representation format. In Proc. of AMIA Annual Symposium, Hanley and Belfus, Philadelphia, PA, USA, 645–649.Google Scholar
Pelletier, F. J., Sutcliffe, G. and Suttner, C. B. 2002. The development of CASC. AI Communications 15 (2–3), 79–90.Google Scholar
Phillips, J. D. and Vojtěchovskiý, P. 2005. Linear groupoids and the associated wreath products. Journal of Symbolic Computation 40 (3), 1106–1125.CrossRefGoogle Scholar
Poole, D. 1990. A methodology for using a default and abductive reasoning system. International Journal of Intelligent System 5 (5), 521–548.CrossRefGoogle Scholar
Reif, W. 1995. The KIV approach to software verification. In KORSO: Methods, Languages, and Tools for the Construction of Correct Software, Broy, M. and Jähnichen, S., Eds. LNCS, Vol. 1009. Springer-Verlag, Berlin, 339–370.CrossRefGoogle Scholar
Richardson, M. and Domingos, P. 2006. Markov logic networks. Machine Learning 62, 1-2, 107–136.CrossRefGoogle Scholar
Robinson, J. A. 1965. Automated deduction with hyperresolution. International Journal of Computatational Mathematics 1, 23–41.Google Scholar
Schmidt, R. A. and Hustadt, U. 2003. Mechanised reasoning and model generation for extended modal logics. In Theory and Applications of Relational Structures as Knowledge Instrument, de Swart, H., Orlowska, E., Schmidt, G. and Roubens, M., Eds. LNCS, Vol. 2929. Springer, Heidelberg, 38–67.CrossRefGoogle Scholar
Shahar, Y. 1997. A framework for knowledge-based temporal abstraction. Artificial Intelligence 90 (1-2), 79–133.CrossRefGoogle Scholar
Shahar, Y. and Cheng, C. 2000. Model-based visualization of temporal abstractions. Computational Intelligence 16 (2), 279–306.CrossRefGoogle Scholar
Shahar, Y., Miksch, S. and 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, 29–51.CrossRefGoogle Scholar
Shepherdson, J. C. 1987. Negation in logic programming. In Deductive Databases and Logic Programming, Minker, J., Ed. Morgan Kaufmann, San Mateo, CA, USA, 19–88.Google Scholar
Shiffman, R. and Greenes, R. 1994. Improving clinical guidelines with logic and decision-table techniques: Application in hepatitis immunization recommendations. Medical Decision Making 14, 245–254.CrossRefGoogle ScholarPubMed
Shortliffe, E. 1974. Mycin: A rule-based computer program for advising physicians regarding antimicrobial therapy selection. Ph.D. Thesis, Stanford University.CrossRefGoogle Scholar
Stärk, R. F. 1994. Input/output dependencies of normal logic programs. Journal of Logic and Computation 4 (3), 249–262.CrossRefGoogle Scholar
Ten Teije, A., Marcos, M., Balser, M., van Croonenborg, J., Duelli, C., van Harmelen, F., Lucas, P., Miksch, S., Reif, W., Rosenbrand, K. and Seyfang, A. 2006. Improving medical protocols by formal methods. Artificial Intelligence in Medicine 36 (3), 193–209.CrossRefGoogle ScholarPubMed
Terenziani, P. 2000. Integrating temporal reasoning with periodic events. Computational Intelligence 16 (2), 210–256.CrossRefGoogle Scholar
Terenziani, P., Molino, G. and Torchio, M. 2001. A modular approach for representing and executing clinical guidelines. Artificial Intelligence in Medicine 23, 249–276.CrossRefGoogle ScholarPubMed
Terenziani, P., Montani, S., Torchio, M., Molino, G. and Anselma, L. 2003. Temporal consistency checking in clinical guidelines acquisition and execution. In Proc. of AMIA Annual Symposium. Washington, DC, USA, 659–663.Google Scholar
Turner, R. 1985. Logics for Artificial Intelligence. Ellis Horwood, Chichester.Google Scholar
van Bemmel, J. and Musen, M., Eds. 2002. Handbook of Medical Informatics. Springer-Verlag, Heidelberg.Google Scholar
Woolf, S. H. 2000. Evidence-based medicine and practice guidelines: An overview. Cancer Control 7 (4), 362–367.CrossRefGoogle ScholarPubMed
Wos, L., Overbeek, R., Lusk, E. and Boyle, J. 1984. Automated Reasoning: Introduction and Applications. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
Wos, L., Robinson, G. and Carson, D. 1965. Efficiency and completeness of the set of support strategy in theorem proving. Journal of the Association for Computing Machinery 12, 536–541.CrossRefGoogle Scholar