Title :
Refining an Automatic EDSS Scoring Expert System for Routine Clinical Use in Multiple Sclerosis
Author :
Gaspari, Mauro ; Saletti, Davide ; Scandellari, Cinzia ; Stecchi, Sergio
Author_Institution :
Dipt. di Sci. dell´´ Inf., Univ. of Bologna, Bologna, Italy
fDate :
7/1/2009 12:00:00 AM
Abstract :
The expanded disability status scale (EDSS) has been the most widely used measure of disability in multiple sclerosis (MS) clinical trials. Although EDSS has the advantage of familiarity with respect to recent proposals, and remains the de facto standard, it is difficult to use consistently between evaluators. Automatic EDSS (AEDSS) is an expert system designed to overcome this problem. It constrains the neurologist to follow precise reasoning steps, enhancing EDSS reliability. In this paper, we show how a deep analysis of the neurological knowledge involved has been essential for adopting AEDSS in routine clinical use. We present an ontology for the EDSS domain and highlight the enhancements to AEDSS due to this additional knowledge. A validation experiment in four MS centers in Italy showed that AEDSS reduces interrater variability, and in many cases, is able to correct errors of neurologists.
Keywords :
biomedical measurement; diseases; neurophysiology; ontologies (artificial intelligence); automatic EDSS scoring expert system; expanded disability status scale; interrater variability; multiple sclerosis; neurological knowledge; ontology; routine clinical use; Medical decision-making; Medical expert systems; Medical services; Nervous system; medical expert systems; medical services; nervous system; Algorithms; Decision Making, Computer-Assisted; Decision Support Systems, Clinical; Disability Evaluation; Expert Systems; Humans; Multiple Sclerosis; Severity of Illness Index;
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
DOI :
10.1109/TITB.2008.926498