Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-30T21:47:41.107Z Has data issue: false hasContentIssue false

Improving adherence to heart failure management guidelines via abductive reasoning*

Published online by Cambridge University Press:  23 August 2017

ZHUO CHEN
Affiliation:
University of Texas at Dallas, Texas, USA (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
ELMER SALAZAR
Affiliation:
University of Texas at Dallas, Texas, USA (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
KYLE MARPLE
Affiliation:
University of Texas at Dallas, Texas, USA (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
GOPAL GUPTA
Affiliation:
University of Texas at Dallas, Texas, USA (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
LAKSHMAN TAMIL
Affiliation:
University of Texas at Dallas, Texas, USA (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
DANIEL CHEERAN
Affiliation:
Cardiology Division, Department of Internal Medicine, University of Texas Southwestern Medical Center, Texas, USA (e-mail: [email protected], [email protected], [email protected])
SANDEEP DAS
Affiliation:
Cardiology Division, Department of Internal Medicine, University of Texas Southwestern Medical Center, Texas, USA (e-mail: [email protected], [email protected], [email protected])
ALPESH AMIN
Affiliation:
Cardiology Division, Department of Internal Medicine, University of Texas Southwestern Medical Center, Texas, USA (e-mail: [email protected], [email protected], [email protected])

Abstract

Management of chronic diseases, such as heart failure, is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing heart failure using answer set programming. In this paper, we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.

Type
Regular Papers
Copyright
Copyright © Cambridge University Press 2017 

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.)

Footnotes

*

This research is supported by NSF (Grant No. 1423419) and the Texas Medical Research Collaborative.

References

Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.CrossRefGoogle Scholar
Cabana, M. D. et al. 1999. Why don't physicians follow clinical practice guidelines?: A framework for improvement. JAMA 282, 15, 14581465. /data/Journals/JAMA/4708/JRV90041.pdf.CrossRefGoogle ScholarPubMed
Chen, Z., Marple, K., Salazar, E., Gupta, G. and Tamil, L. 2016. A physician advisory system for chronic heart failure management based on knowledge patterns. TPLP 16, 5-6, 604618.Google Scholar
Chesani, F., Mello, P., Montali, M. and Storari, S. 2007. Testing careflow process execution conformance by translating a graphical language to computational logic. In Proc. of Artificial Intelligence in Medicine, 11th Conference on Artificial Intelligence in Medicine, AIME 2007, July 7–11, 2007, Amsterdam, The Netherlands, 479–488.Google Scholar
Douven, I. 2011. Abduction. In The Stanford Encyclopedia of Philosophy, Zalta, E. N., Ed.Google Scholar
Fox, J., Johns, N. and Rahmanzadeh, A. 1998. Disseminating medical knowledge: The proforma approach. Artificial Intelligence in Medicine 14, 1–2, 157182.CrossRefGoogle ScholarPubMed
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2014. Clingo = ASP + control: Preliminary report. CoRR abs/1405.3694.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The Stable Model Semantics for Logic Programming. MIT Press, 10701080.Google Scholar
Go, A. S. et al. 2013. Heart Disease and Stroke statistics 2013 Update: A Report from the American Heart Association. Technical Report.Google Scholar
Groot, P., Hommersom, A., Lucas, P. J., Merk, R.-J., ten Teije, A., van Harmelen, F. and Serban, R. 2009. Using model checking for critiquing based on clinical guidelines. Artificial Intelligence in Medicine 46, 1, 1936.CrossRefGoogle ScholarPubMed
Group, T. M. N. 2006. Enhancing the use of clinical guidelines: A social norms perspective. Journal of the American College of Surgeons 202, 5, 826836.Google Scholar
Gupta, G., Bansal, A., Min, R., Simon, L. and Mallya, A. 2007. Coinductive logic programming and its applications. In Proc. of 23rd International Conference on Logic Programming, ICLP 2007, September 8–13, 2007, Porto, Portugal, 27–44.Google Scholar
Harman, G. H. 1965. The inference to the best explanation. The Philosophical Review 74, 1, 8895.CrossRefGoogle Scholar
Inoue, K. 1991. Extended logic programs with default assumptions. In Proc. of the 8th International Conference on Logic Programming, June 24–28, 1991, Paris, France, 490–504.Google Scholar
Jacobs, A. K. et al. 2013. ACCF/AHA clinical practice guideline methodology summit report: A report of the american college of cardiology foundation/american heart association task force on practice guidelines. Journal of the American College of Cardiology 61, 2, 213265. /data/Journals/JAC/926164/09025.pdf.CrossRefGoogle Scholar
Kakas, A. C., Kowalski, R. A. and Toni, F. 1992. Abductive logic programming. Journal of Logic and Computation 2, 6, 719770.CrossRefGoogle Scholar
Marek, V. W. and Truszczyński, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: A 25-Year Perspective, Apt, K. R., Marek, V. W., Truszczynski, M. and Warren, D. S., Eds. Springer, Berlin, Heidelberg, 375398.CrossRefGoogle Scholar
Marple, K., Bansal, A., Min, R. and Gupta, G. 2012. Goal-directed execution of answer set programs. In Proc. of Principles and Practice of Declarative Programming, PPDP'12, September 19–21, 2012, Leuven, Belgium, 35–44.Google Scholar
Marple, K. and Gupta, G. 2012. Galliwasp: A goal-directed answer set solver. In Proc. of Logic-Based Program Synthesis and Transformation, 22nd International Symposium, LOPSTR 2012, Revised Selected Papers, September 18–20, 2012, Leuven, Belgium, 122–136.Google Scholar
Marple, K., Salazar, E. and Gupta, G. 2016a. Computing Stable Models of Normal Logic Programs Without Grounding. Forthcoming.Google Scholar
Marple, K., Salazar, E. and Gupta, G. 2016b. s(ASP). URL: https://sourceforge.net/projects/sasp-system/.Google Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 3–4, 241273.CrossRefGoogle Scholar
Satoh, K. and Iwayama, N. 1991. Computing abduction by using the TMS. In Proc. of the 8th International Conference on Logic Programming, June 24–28, 1991, Paris, France, 505–518.Google Scholar
Spiotta, M., Terenziani, P. and Dupré, D. T. 2015. Answer set programming for temporal conformance analysis of clinical guidelines execution. In KR4HC/ProHealth.CrossRefGoogle Scholar
Yancy, C. W. et al. 2013. 2013 ACCF/AHA guideline for the management of heart failure: A report of the american college of cardiology foundation/american heart association task force on practice guidelines. Journal of the American College of Cardiology 62, 16, e147.CrossRefGoogle Scholar