Development of a Knowledge-based Clinical Decision Support System for Multiple Sclerosis Diagnosis.

The diagnosis of multiple sclerosis (MS) is difficult considering its complexity, variety in signs and symptoms, and its similarity to the signs and symptoms of other neurological diseases. The purpose of this study is to design and develop a clinical decision support system (CDSS) to help physicians diagnose MS with a relapsing-remitting phenotype. The CDSS software was developed in four stages: requirement analysis, system design, system development, and system evaluation.

An Expert System to Diagnose Pneumonia Using Fuzzy Logic.

Introduction: Pneumonia is the most common and widespread killing disease of respiratory system which is difficult to diagnose due to identical clinical signs of respiratory system.

Aim: In this research, to diagnose this, a structure of a fuzzy expert system has been offered. This is done in order to help general physicians and the patients make decision and also differentiate among chronic bronchitis, tuberculosis, asthma, embolism, lung cancer.

Intelligent Computer Systems for Multiple Sclerosis Diagnosis: a Systematic Review of Reasoning Techniques and Methods.

Objective: Intelligent computer systems are used in diagnosing Multiple Sclerosis and help physicians in the accurate and timely diagnosis of the disease. This study focuses on a review of different reasoning techniques and methods used in intelligent systems to diagnose MS and analyze the application and efficiency of different reasoning methods in order to find the most efficient and applicable methods and techniques for MS diagnosis.

Methods: A complete research was carried out on articles in various electronic databases based on Mesh vocabulary.