Medical Case Based Reasoning Frameworks

Abstract

Case-Based Reasoning (CBR) is one of the most suitable AI techniques for building clinical decision support systems. Medical domain complexity introduces many challenges for building these systems. Building the systems' knowledge base from the Electronic Health Record (EHR), the encoding of case-base knowledge with standard medical ontology, and the handling of vague data are examples of these challenges. Although several advantages of using CBR in medicine have been identified, there are no real systems acceptable to physicians. This systematic review examines the current state of CBR and its limitations in the medical domain, especially for diabetes mellitus. The critical evaluation of the status of diabetes CBR systems presents unique opportunities for improving these systems. The literature review covers most of the English language studies extracted from relevant databases by using search terms relating CBR, ontology, Fuzzy, and standard terminology concepts. The authors identify 38 articles published between 1999 and 15 January 2015, which represent original researches in CBR domain. The study includes 15 (39.5%) non-medical studies and 23 (60.5%) medical studies with ~22% for diabetes CBR. A list of 18 integrated evaluation metrics has been proposed and used in the analysis. The results show that the non-medical CBR systems achieved higher advances (50%) than medical systems (42.9%). In addition, the diabetes management CBR systems achieve the lowest advances (21.4%) compared to other systems. These shortages explain the question “why CBR paradigm are not fully utilized in the commercial medical systems?” As a result, there is a distinct need for more comprehensive enhancements in clinical CBR especially diabetes systems.

A Real-Time Entity Monitoring based on States and Scenarios

“…On the other hand, another person uses a mercurial thermometer using an axillary method applied to the same entity. Even when they are trying to get a corporal temperature and the values are expressed under the same range, scale and unit, both results are not comparable because the oral temperature is different from the axillary temperature with differences between 0.3ºC and 0.6ºC [22]. The measurement is transversal to different disciplines and industries, a reason for which there exists heterogeneity about the application environments and the involved concepts (from the measurement target up to how to interpret each value in the light of a concept).…”

Telemedicine in Arab Countries: Innovation, Research Trends, and Way Forward

“…Top citer for the first cluster was (19) from Helwan University, Cairo, Egypt: presenting a review on swarm and evolutionary computing approaches for deep learning (19). In cluster 2, the most active citer was El-Sappagh et al based at Minia University in Egypt, who published three review papers on medical case reasoning frameworks, SNOMED CT ontology and mobile health technologies for diabetes mellitus (20)(21)(22). The last subcluster was actively cited by Syaed et al based at Cairo University in Egypt, who published his empirical work on binary whale optimization algorithm and binary moth flame optimization with clustering algorithms for clinical breast cancer diagnoses (23) (Figures 5 and 6).…”