A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis
TL;DR: This paper proposes a fuzzy ontology-based CBR framework that combines a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types and achieves an accuracy of 97.67%.
About: This article is published in Artificial Intelligence in Medicine.The article was published on 2015-11-01. It has received 98 citations till now. The article focuses on the topics: Neuro-fuzzy & Ontology (information science).
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TL;DR: A systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular.
Abstract: The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.
811 citations
Cites background from "A fuzzy-ontology-oriented case-base..."
...In [76], authors proposed a fuzzy ontology-based Case-based reasoning (CBR) framework, mimicking expert thinking, further tested on diabetes diagnosis problems....
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TL;DR: A type-2 fuzzy ontology–aided recommendation systems for IoT-based healthcare to efficiently monitor the patient's body while recommending diets with specific foods and drugs and the experimental results show that the proposed system is efficient for patient risk factors extraction and diabetes prescriptions.
130 citations
TL;DR: An analysis of the detection, diagnosis, and self-management techniques of DM from six different facets viz., datasets of DM, pre-processing methods, feature extraction methods, machine learning-based identification, classification, and diagnosis ofDM, artificial intelligence-based intelligent DM assistant and performance measures are delivered.
83 citations
Cites background from "A fuzzy-ontology-oriented case-base..."
...El-Sappagh et al. (2015) propose a fuzzy ontology-based casebased justification paradigm for the treatment of DM....
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...…et al. (2017), Xu and Wang (2019), Jahangir et al. (2017), AlZebari and Sengur (2019), Kriještorac et al. (2019), El-Sappagh et al. (2015), Sun et al. (2019), Shanthi et al. (2019), Sultan (2020), Jayashree and Kumar (2019), Benbelkacem and Atmani (2019) Nnamoko…...
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TL;DR: A distance measure named weighted heterogeneous value distance metric, which can better deal with both continuous and discrete attributes simultaneously than the standard Euclidean distance, and a genetic algorithm for learning the attribute weights involved in this distance measure automatically are used.
75 citations
TL;DR: The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers—doctors and nurses—in this field.
Abstract: In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future All these parameters, when analyzed, can be helpful for patients and doctors' decision support Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas This recently needed knowledge includes AI tools, which have become part of the diabetes health care The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field
70 citations
References
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TL;DR: The role of ontology in supporting knowledge sharing activities is described, and a set of criteria to guide the development of ontologies for these purposes are presented, and it is shown how these criteria are applied in case studies from the design ofOntologies for engineering mathematics and bibliographic data.
Abstract: Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.
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TL;DR: An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.
Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.
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27 Jun 1994
Abstract: This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.
3,492 citations
Posted Content•
TL;DR: This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT), and sees the approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems.
Abstract: This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.
659 citations