In the primary health care center of Mjölby a sample of case notes in the ear-nose-and-throat realm (N=425) was computer processed using an inductive rule-based decision-tree generating program. As a result of incomplete information in the case-files, the decision trees were “noisy,” e.g., had branches and leaves without meaning. This led to a need for “pruning.” Various methods were tried. The effects of different methods of decision-tree generating and pruning are discussed. The choice of root argument and branching of the decision-trees suggested by the software was the most clinically applicable. The “statistic” approach to pruning gave the most compact and still most clinically relevant decision-tree. The pruned and edited decision trees are compared with a previously published preliminary essential data set for the ear-nose-and-throat realm in primary health care and then discussed as a possible decision support system for various primary health care groups in a practice setting.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Miller, R. A., Medical diagnostic decision-support systems past, present and future: A threaded bibliography and brief commentary.J. Am. Med Inform. Assoc 1:8–27, 1994.
Moidu, K., and Wigertz, O., Computer-based information systems in primary health care—why?J. Med. Syst. 13:59–65, 1989.
Moidu, K., Application of an essential data set based computer system in support of maternal and child care.Int. J. Biomed. Comput. 31:159–175, 1992.
Taylor, T. R., The computer and clinical decision-support systems in primary care.J. Fam. Prac. 30:137–140, 1990.
Buchanan, B. G., and Duda, R. O., Principles of rule-based expert systems.Advances in Computers, In (Yovits M., ed.), Academic Press, New York, Vol. 22, 1982.
Ebell, M. H., Using decision rules in primary care practice.Primary Care 22:319–340, 1995.
Elson, R. B., and Conelly, D. P., Computerised decision support systems in primary care.Primary Care 22:365–384, 1995.
Knotterus, J. A., Diagnostic prediction rules: Principles, requirements and pitfalls.Primary Care 22:341–363, 1995.
Wasson, J. H., Clinical prediction rules: Applications and methodological standards.New Engl. J. Med. 313:793–799, 1985.
Clancey, W. J., and Shortliffe, E. H.,Readings in Medical Artificial Intelligence, The First Decade, Addison-Wesley Publishing Company, Menlo Park, CA, 1984.
Corlett, R. A., Explaining induced decision trees. InProceedings Expert Systems '83 Churchill College, Cambridge, England, 1983, pp. 136–142.
Lister, R., Electronic fault diagnosis: Fault trees and expert systems.Applications of Expert Systems, In (Quinlan, J. R., ed.), Addison-Wesley Publishing Company, Sydney, pp. 2266–2289, 1989.
Michie, D., Current developments in expert systems.Applications of Expert Systems, In (Quinlan, J. R., ed.), Addison-Wesley Publication Company, Sydney, pp. 1137–56, 1987.
Utgoff, P. E.,Decision Tree Induction Based on Efficient Tree Restructuring, Technical Report (95-18), Dept. of Computer Science, Univ. of Mass., 1995.
Young, M. J., McMahon, L. F., and Stross, J. K., Prediction rules for patients with suspected myocardial infarction. Applying guidelines in community hospitals.Arch. Intern. Med. 147:1219–1222, 1985.
af Klercker, T., Trell, E., and Lundqvist, P. G., Towards an essential data set for ambulatory otorhinolaryngological care in general practice.J. Med. Informatics 19:253–267, 1994.
Övretveit, J.,Health Service Quality, Blackwell Scientific Publications, London, 1992.
af Klercker, T., Trell, E., and Lundqvist, P. G., Essential data set for ambulatory ear-, nose- and throat-care in general practise: A help in quality management,Quality in Health Care, 1996 (in press).
Agreement between the Swedish Associations of: General Medicine, District Doctors and Oto-rhino-laryngology on guidelines for management and collaboration regarding patients with ENT-diseases (The PRIMEAR-agreement) (In Swedish). Swedish Medical Association, stencil U 80/84, 1984.
Abib, B., and Michie, D., Generating rules from examples inProc. of Ninth IJCAI Los Angeles, 1985.
O'Neill, J. L., Knowledge acquisition for radar classification.Application of Expert Systems, In (Quinlan, R., ed.), Addison-Wesley Publishing Company, Sydney, pp. 1184–1919, 1987.
O'Rourke, P., A comparative study of inductive learning systems AQ11 and ID 3 using a chess end-game test problem. Technical Report, University of Illinois at Urbana-Campaign, 1982.
Quinlan, J. R., Learning from noisy data.Proceedings of the International Machine Learning Workshop, Illinois, 1983.
About this article
Cite this article
af Klercker, T. Effects of pruning of a decision-tree for the ear, nose, and throat realm in primary health care based on case-notes. J Med Syst 20, 215–226 (1996). https://doi.org/10.1007/BF02263393
- decision support
- case based