A new decision support model for preanesthetic evaluation. Issue 133 (September 2016)

• Record Type: Journal Article
• Authors: Sobrie, Olivier; Lazouni, Mohammed El Amine; Mahmoudi, Saïd; Mousseau, Vincent; Pirlot, Marc
• Is Part Of: Computer methods and programs in biomedicine. Issue 133(2016)
• Page Start: 183
• Page End: 193
• Publication Date: 2016-09
• Subjects: Multiple criteria decision support -- Preanesthesia evaluation -- ASA score -- Classification -- MR-Sort; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.05.021
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a method to learn patient's ASA score and admission to surgery decision. Classification accuracy exceeds 95% and compares favorably to state-of-the-art machine learning algorithms. The classification model is easily interpretable by physicians. Abstract: Background and objective: The principal challenges in the field of anesthesia and intensive care consist of reducing both anesthetic risks and mortality rate. The ASA score plays an important role in patients' preanesthetic evaluation. In this paper, we propose a methodology to derive simple rules which classify patients in a category of the ASA scale on the basis of their medical characteristics. Methods: This diagnosis system is based on MR-Sort, a multiple criteria decision analysis model. The proposed method intends to support two steps in this process. The first is the assignment of an ASA score to the patient; the second concerns the decision to accept—or not—the patient for surgery. Results: In order to learn the model parameters and assess its effectiveness, we use a database containing the parameters of 898 patients who underwent preanesthesia evaluation. The accuracy of the learned models for predicting the ASA score and the decision of accepting the patient for surgery is assessed and proves to be better than that of other machine learning methods. Furthermore, simple decision rules can be explicitly derived from the learned model. These are easily interpretable by doctors, and their consistency with medical knowledge can be checked. Conclusions: The proposed model for assessing the ASA score produces accurate predictions on the basis of the (limited) set of patient attributes in the database available for the tests. Moreover, the learned MR-Sort model allows for easy interpretation by providing human-readable classification rules.