Rough Classification of Pneumonia Patients using a Clinical Database
This study used the original model of rough sets  for data analysis of objective clinical findings from pneumonia patients. Pawlak’s rough classification algorithm  was used to find the reduct, which is a logical construct of the most information-preserving findings from a decision table. The condition attributes were the clinical findings that were used by a hospital information system, MedisGroups, as independent variables in the disease severity scoring algorithm for Bacterial Lung Infection or Other Lung Infection diseases. The International Classification of Diseases (ICD) code on the patient’s medical record was used as the decision attribute. The condition attributes not included in the reduct are considered superfluous with respect to the decision attribute. Six of the twenty-five condition attributes formed the reduct.
Some diseases, such as pneumonia, do not have a gold standard for validating a diagnosis. Iliad, an expert system based on Bayes’ Theorem, was chosen for evaluation of the rough classification results. The same subset of condition attributes appeared in both the rough sets logical classifier and Iliad’s probabilistic classifier.
In addition, a machine learning system, LERS (Learning from Examples based on Rough Sets), was used to induce rules from the decision table.
KeywordsHospital Information System Decision Table Decision Attribute Pneumonia Patient Machine Learning System
Unable to display preview. Download preview PDF.
- Ziarko W., Analysis of Uncertain Information in the Framework of Variable Precision Rough Sets Foundations of Computing and Decision Sciences (1993), 18(3–4), pp. 381–396. Google Scholar
- Szladow A., Ziarko W., Rough Sets: Working with Imperfect Data, AI Expert (1993), 8 (7), pp. 36–39.Google Scholar
- Ziarko W. (ed.) Proceedings of the International Workshop on Rough Sets and Knowledge Discovery RSKD ‘83, (1993), Banff, Alberta. Google Scholar
- MedisGroups Scoring Algorithm: A Technical Description, MediQual Systems (1993).Google Scholar
- Hashemi R.R., Jeolovsek F.R., Razzaghi M., Developmental Toxicity Risk Assessment: A Rough Sets Approach Methods of Information in Medicine, (1993), 32, pp. 47–54. Google Scholar
- Grzymala-Busse J., LERS-A System for Learning from Examples Based on Rough Sets Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Roman Slowinski (ed.), Kluwer Academic Publishers, (1992), pp. 3–18. Google Scholar
- Lliad User’s Manual Version 4.1 (1992), Applied Informatics Inc., Salt Lake City, Utah.Google Scholar
- Johnson C.C., Martin M., Epstein S.M., Lee J.D., The Effect of a Physician Education Program on Hospital Length of Stay and Total Patient Charges, The Journal of the South Carolina Medical Association (1993), June, pp. 293–301.Google Scholar