Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases.

This paper employs the Analytical Hierarchy Process (AHP) to enhance the accuracy of differential diagnosis for febrile diseases, particularly prevalent in tropical regions where misdiagnosis may have severe consequences. The migration of health workers from developing countries has resulted in frontline health workers (FHWs) using inadequate protocols for the diagnosis of complex health conditions. The study introduces an innovative AHP-based Medical Decision Support System (MDSS) incorporating disease risk factors derived from physicians' experiential knowledge to address this challenge.

The impact of a short-term training program on workers' sterile processing knowledge and practices in 12 Ethiopian hospitals: A mixed methods study.

Background: The need for increased attention to surgical safety in low- and middle-income countries invited organizations worldwide to support improvements in surgical care. However, little is written about issues in instrument sterilization in low- and middle-income countries including Ethiopia.

Objective: The study aims to identify the impact of a sterile processing course, with a training-of-trainers component and workplace mentoring on surgical instrument cleaning and sterilization practices at 12 hospitals in Ethiopia.

A neuro-fuzzy decision support system for the diagnosis of heart failure.

A neuro-fuzzy decision support system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The neural networks employ a multi-layers perception back propagation learning process while the fuzzy logic uses the root sum square inference procedure.

Experimental study of fuzzy-rule based management of tropical diseases: case of malaria diagnosis.

The application of the conventional symbolic rules found in knowledge base technology to the management of a disease suffers from its inability to evaluate the degree of severity of a symptom and by extension the degree of the illness. Fuzzy logic technology provides a simple way to arrive at a definite conclusion from vague, ambiguous, imprecise and noisy data (as found in medical data) using linguistic variables that are not necessary precise. In order to achieve this, a study of a knowledge base system for the management of diseases was undertaken.