Decision Support System For Medical Diagnosis Research Articles

Medical diagnosis decision support: online system

This project designs and implements an online medical diagnosis decision support system. The challenges faced by patients in accessing medical consultation, necessitated the developer to develop a user-friendly system to tackle these challenges. The system provides support on diagnosis decisions for the medically inclined. The system allows the study of the influence of the various symptoms that affect diagnosis. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various diseases. An exhaustive evaluation of the system by comparison with published cases is presented in this project. The overall result shows that the system is functional and reliable. The system was developed using Nodejs and MYSQL technologies implementing fuzzy logic.Keywords: Patience, Diagnosis, Preponderance, Practitioner, Fuzzy InferenceVol. 26 No 1 June 2019

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived.

A SOA‐based medical diagnosis decision support system using the Bayesian theorem and web service technology

This study employs the framework of web services in conjunction with the Bayesian theorem to construct a SOA‐based medical decision support system for diagnosis. The purpose is to help users (physicians) with issues pertinent to medical diagnosis decisions. Users, through the system, key in available prior probabilities, and through computation based on the Bayesian theorem, obtain more accurate diagnosis. The process helps users enhance the quality and efficiency of medical decisions. On the other hand, features of web services enable this medical decision support system to offer more service platforms than the conventional one. Users will have access to the system whether they use Windows, Macintosh, Linux or any other platforms that connect with the Internet via HTTP. More important is the fact that after the system is complete, all web system developers will be able to access the system as a software element freely and quickly. This way, the goal of this study ‐ to provide a medical diagnosis decision support tool and speedily integrate heterogeneous medical decision support systems can be effectively achieved.