Shaker El-Sappagh

A Decision Support System for Diabetes Mellitus Management

Diabetes mellitus is considered as a dangerous chronic disease. Diagnosis is the first step in its management. Clinical decision support system (CDSS) for diabetes diagnosis improves its detection and decreases the opportunity for its complications. However, its diagnosis is a theory-less problem. Case-based reasoning (CBR) is a problem-solving paradigm that uses past experiences to solve new problems. Integration of CBR and formal ontologies enhances the intelligence of this paradigm. Utilizing patients’ electronic health records (EHRs) for building case-base knowledge solves the problem of knowledge acquisition bottleneck; however, preparation steps are required. Moreover, using standard medical ontologies, such as SNOMED-CT, enhances the interoperability and integration of CDSS with the healthcare system. If ontology-based CBR systems utilize vague or imprecise knowledge, the semantic effectiveness is further improved. This paper proposes an advanced and complete fuzzy-ontology-based CBR framework that manages and utilizes imprecise knowledge. We implement the most critical steps in CBR (i.e., case representation and retrieval). The implemented framework has been tested on the diabetes diagnosis problem using a case-base of 60 real cases from The EHR of the Mansoura University Hospitals, Mansoura, Egypt. The proposed system has an accuracy of 97.67%.

DDO: a diabetes mellitus diagnosis ontology

Diabetes mellitus is a major cause of morbidity and mortality in humans. Early diagnosis is the first step toward the management of this condition. However, a diagnosis involves several variables, which makes it difficult to arrive at an accurate and timely diagnosis and to construct accurate personalized treatment plans. An electronic health record system requires an integrated decision support capability, and ontologies are rapidly becoming necessary for the design of efficient, reliable, extendable, reusable, and semantically intelligent knowledge bases. In this study, we take the first step in this direction, by designing an OWL2 diabetes diagnosis ontology (DDO). Protégé 5 software was used for the construction of the ontology. DDO is developed within the framework of the basic formal ontology and the ontology for general medical science to represent entities in the domain of diabetes, and it follows the design principles recommended by the Open Biomedical Ontology Foundry. Currently, DDO contains 6444 concepts, 48 properties, 13,551 annotations, and 27,127 axioms. DDO can serve as a diabetes knowledge base and supports automatic reasoning. It represents a major step toward the development of a new generation of patient-centric decision support tools. DDO is available through BioPortal at: http://www.bioportal.bioontology.org/ontologies/DDO.

Case Based Reasoning: Case Representation Methodologies

Case Based Reasoning (CBR) is an important technique in artificial intelligence, which has been applied to various kinds of problems in a wide range of domains. Selecting case representation formalism is critical for the proper operation of the overall CBR system. In this paper, we survey and evaluate all of the existing case representation methodologies. Moreover, the case retrieval and future challenges for effective CBR are explained. Case representation methods are grouped in to knowledge-intensive approaches and traditional approaches. The first group overweight the second one. The first methods depend on ontology and enhance all CBR processes including case representation, retrieval, storage, and adaptation. By using a proposed set of qualitative metrics, the existing methods based on ontology for case representation are studied and evaluated in details. All these systems have limitations. No approach exceeds 53% of the specified metrics. The results of the survey explain the current limitations of CBR systems. It shows that ontology usage in case representation needs improvements to achieve semantic representation and semantic retrieval in CBR system.

A proposed SNOMED CT ontology-based encoding methodology for diabetes diagnosis case-base

Domain knowledge ontology supports the implementation of intelligent Case Based Reasoning (CBR) systems. Standardized terminologies support efficient indexing and processing of patient data. It is an essential element for the implementation of knowledge-based clinical decision support by exploiting pre-defined semantic relationships, both hierarchical and non-hierarchical in nature. Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) is the most comprehensive and complete terminology. This paper proposes an encoding methodology for clinical data using SNOMED CT. A case study for a diabetes diagnosis data set will be tested where SNOMED CT provides a concept coverage of ~75% for its clinical terms. Custom codes will be provided for uncovered terms. The encoded data set is derived from electronic health record database, and it represents a case base knowledge. The collected concept IDs will be used to build a domain ontology for diabetes diagnosis CBR. This ontology contains 550 concept IDs. The encoded case base and the domain ontology can be used to build a knowledge intensive CBR.

A standard fragment of EHR relational data model for diabetes mellitus diagnosis

Clinical Decision Support System (CDSS) helps in early detection and diagnosis of diabetes. Case Based Reasoning (CBR)-based CDSS is the applicable technique for these ill formed problems. Building CBR case-base is the most critical challenge. Electronic health record (EHR) is the complete source of cases for CBR. However, mapping EHR into case-base is challenge. This paper proposes a standard relational data model for diabetes diagnosis based on HL7 REVI, EHR and SNOMED CT. This data model will work as an operational data store to extract diabetes specific data elements from EHR, standardize its structure using REVI, standardize its data fields using diabetes diagnosis common data elements, standardize its contents using a standard terminology (SNOMED CT), and organize data in the form of problem-solution form to be suitable for case-base format. This case-base will be used as a knowledge base for a diabetes diagnosis CBR system.

Distributed electronic health record based on semantic interoperability using fuzzy ontology: a survey

Abstract Over the past years, the knowledge and data are rising rapidly with the development of communication technology. There is a growing demand for integrating data, especially the medical healthcare data. Electronic Health Record (EHR) provides the patient with secure, real-time, and reliable access to the health record information at any time where any location is needed. Semantic interoperability plays an essential role in improving the medical decision-making, lowering the costs of healthcare, and improving the healthcare quality. Semantic concerns on the study of meanings and interoperability refer to getting systems to work together. There are many models try to solve the EHR semantic interoperability problem. Many different e-health standards are proposed, but they had some problems. Some of these standards do not support full semantic interoperability, whereas some others are poor community support. This paper focuses on the following goals: (1) realizing the urgent need for interoperability in EHR to improve healthcare quality; (2) discussing some of its main problems; (3) surveying some of the existing standards for solving its problems; (4) discussing some key issues required during achieving it; and (5) trying to solve that problem by recommending fuzzy ontology as an intelligent information system solution.

EMD: entity mapping diagram for automated extraction, transformation, and loading processes in data warehousing

During the last few years, researchers and developers had proposed various trials to put a standard conceptual design of ETL processes in data warehouse. These trials try to represent the main mapping activities at the conceptual level. Due to limitations of the previous trials, in this paper 1) We propose a model for conceptual design of ETL processes and we call it entity mapping diagram (EMD). The proposed model is built upon the enhancement of the models in the previous work to support some missing mapping features. 2) We implemented the proposed conceptual model in a prototype called EMD Builder and use it in an illustration scenario.