Guideline Formalization and Knowledge Representation for Clinical Decision Support

General information

Authors:
Tiago OLIVEIRA, José NEVES, Paulo NOVAIS

DOI:
10.14201/ADCAIJ201212111

Volume:
Regular Issue 1 (2), 2012

Keywords:

Computer-Interpretable; Guidelines; Incomplete Information; Quality of Information; Decision Support

The prevalence of situations of medical error and defensive medicine in healthcare institutions is a great concern of the medical community. Clinical Practice Guidelines are regarded by most researchers as a way to mitigate these occurrences; however, there is a need to make them interactive, easier to update and to deploy. This paper provides a model for Computer-Interpretable Guidelines based on the generic tasks of the clinical process, devised to be included in the framework of a Clinical Decision Support System. Aiming to represent medical recommendations in a simple and intuitive way. Hence, this work proposes a knowledge representation formalism that uses an Extension to Logic Programming to handle incomplete information. This model is used to represent different cases of missing, conflicting and inexact information with the aid of a method to quantify its quality. The integration of the guideline model with the knowledge representation formalism yields a clinical decision model that relies on the development of multiple information scenarios and the exploration of different clinical hypotheses.

References

BRENNAN, T. A., The Institute of Medicine report on medical errors-could it do harm? The New England Journal of Medicine 96(6)(2000) 1123–1125

CHAWLA, GUNDERMAN, R. B., Defensive medicine: prevalence, implications, and recommendations. Academic radiology 15(7) (2008) 948–9

Fox, J., Johns, N., Rahmanzadeh, A., Disseminating medical knowledge: the PROforma approach. Artificial Intelligence in Medicine 14(2) (1998)157-182

KALRA, J., Medical errors: an introduction to concepts. Clinical biochemistry 37 (12) (2004) 1043–51

NEVES, J., A Logic Interpreter to handle Time and Negation in Logic Databases, in: ACM '84 Proceedings of the 1984 annual conference of the ACM on The fifth generation challenge, New York, 1984, pp. 50-54

NEVES, J., RIBEIRO, J., PEREIRA, P., et al., Evolutionary intelligence in asphalt pavement modeling and quality-of-information. Progress in Artificial Intelligence 1(1) (2012) 1–17

NOVAIS, P., SALAZAR, M., RIBEIRO, J., ANALIDE, C., NEVES, J., Decision Making and Quality-of-Information, in Arroyo, A., Corchado, E., Tricio, V. (Eds), Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010), 2010, pp. 187–195

OLIVEIRA, T., COSTA, A., NEVES, J., NOVAIS, P., Digital Clinical Guidelines Modelling, in Omatu S, Paz Santana J. F., González S. R., Molina J. M., Bernardos A. M., Corchado Rodríguez J. M. (Eds.), Modelling and Simulation 2011, EUROSIS, ISBN: 978-9077381-66-3, 2011, pp. 392-398

Patel, V. L., Allen, V. G., Arocha, J. F., Shortliffe, E. H., Representing Clinical Guidelines in GLIF. Journal of the American Medical Informatics Association 5(5) (1998) 467–483

ROSENBRAND, K., VAN CROONENBORG, J., WITTENBERG, J., Guideline Development, in A. ten Teije, S. Miksch, P. Lucas (Eds.), Computer-based Medical Guidelines and Protocols: A Primer and Current Trends, 2008, pp. 3–22.

SAMWALD, M., FEHRE, K., DE BRUIN, J., ADLASSNIG, K. P., The Arden Syntax standard for clinical decision support: Experiences and directions. Journal of Biomedical Informatics 45(4) (2012) 711-8

Shahar, Y., Miksch, S., Johnson, P. , The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines, Artificial intelligence in Medicine 14(2) (1998) 29–51

STRASZECKA, E., Combining uncertainty and imprecision in models of medical diagnosis. Information Sciences 176(20) (2006) 3026-3059

TU, S. W., CAMPBELL J. R., GLASGOW, J., et al., The SAGE Guideline Model: achievements and overview. JAMIA 14(5) (2007) 589–98