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Pérez-Gaspar M, Gomez J, Bárcenas E, Garcia F. A fuzzy description logic based IoT framework: Formal verification and end user programming. PLoS One 2024; 19:e0296655. [PMID: 38517840 PMCID: PMC10959344 DOI: 10.1371/journal.pone.0296655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/15/2023] [Indexed: 03/24/2024] Open
Abstract
The Internet of Things (IoT) has become one of the most popular technologies in recent years. Advances in computing capabilities, hardware accessibility, and wireless connectivity make possible communication between people, processes, and devices for all kinds of applications and industries. However, the deployment of this technology is confined almost entirely to tech companies, leaving end users with only access to specific functionalities. This paper presents a framework that allows users with no technical knowledge to build their own IoT applications according to their needs. To this end, a framework consisting of two building blocks is presented. A friendly interface block lets users tell the system what to do using simple operating rules such as "if the temperature is cold, turn on the heater." On the other hand, a fuzzy logic reasoner block built by experts translates the ambiguity of human language to specific actions to the actuators, such as "call the police." The proposed system can also detect and inform the user if the inserted rules have inconsistencies in real time. Moreover, a formal model is introduced, based on fuzzy description logic, for the consistency of IoT systems. Finally, this paper presents various experiments using a fuzzy logic reasoner to show the viability of the proposed framework using a smart-home IoT security system as an example.
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Affiliation(s)
- Miguel Pérez-Gaspar
- Department of Telecommunications, National Autonomous University of Mexico, Mexico City, Mexico
| | - Javier Gomez
- Department of Telecommunications, National Autonomous University of Mexico, Mexico City, Mexico
| | - Everardo Bárcenas
- Department of Computer Engineering, National Autonomous University of Mexico, Mexico City, Mexico
| | - Francisco Garcia
- Department of Telecommunications, National Autonomous University of Mexico, Mexico City, Mexico
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2
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Ayyanu R, Arul A, Song N, Anand Babu Christus A, Li X, Tamilselvan G, Bu Y, Kavitha S, Zhang Z, Liu N. Wearable sensor platforms for real-time monitoring and early warning of metabolic disorders in humans. Analyst 2023; 148:4616-4636. [PMID: 37712440 DOI: 10.1039/d3an01085f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Nowadays, the prevalence of metabolic syndromes (MSs) has attracted increasing concerns as it is closely related to overweight and obesity, physical inactivity and overconsumption of energy, making the diagnosis and real-time monitoring of the physiological range essential and necessary for avoiding illness due to defects in the human body such as higher risk of cardiovascular disease, diabetes, stroke and diseases related to artery walls. However, the current sensing techniques are inconvenient and do not continuously monitor the health status of humans. Alternatively, the use of recent wearable device technology is a preferable method for the prevention of these diseases. This can enable the monitoring of the health status of humans in different health domains, including environment and structure. The use wearable devices with the purpose of facilitating rapid treatment and real-time monitoring can decrease the prevalence of MS and long-time monitor the health status of patients. This review highlights the recent advances in wearable sensors toward continuous monitoring of blood pressure and blood glucose, and further details the monitoring of abnormal obesity, triglycerides and HDL. We also discuss the challenges and future prospective of monitoring MS in humans.
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Affiliation(s)
- Ravikumar Ayyanu
- School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Amutha Arul
- Department of Chemistry, Francis Xavier Engineering College, Tirunelveli 627003, India
| | - Ninghui Song
- Nanjing Institute of Environmental Science, Key Laboratory of Pesticide Environmental Assessment and Pollution Control, Ministry of Ecology and Environment, Nanjing 210042, China.
| | - A Anand Babu Christus
- Department Chemistry, SRM Institute of Science and Technology, Ramapuram Campus, Ramapuram-600089, Chennai, Tamil Nadu, India
| | - Xuesong Li
- School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - G Tamilselvan
- School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Yuanqing Bu
- Nanjing Institute of Environmental Science, Key Laboratory of Pesticide Environmental Assessment and Pollution Control, Ministry of Ecology and Environment, Nanjing 210042, China.
| | - S Kavitha
- Department of Chemistry, The M.D.T Hindu college (Affiliated to Manonmanium Sundaranar University), Tirunelveli-627010, Tamil Nadu, India
| | - Zhen Zhang
- School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Nan Liu
- Institute of Environment and Health, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, P. R. China.
- Institute of Chronic Disease Risks Assessment, School of Nursing and Health, Henan University, Kaifeng, 475004, P. R. China
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3
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Kermani F, Zarkesh MR, Vaziri M, Sheikhtaheri A. A case-based reasoning system for neonatal survival and LOS prediction in neonatal intensive care units: a development and validation study. Sci Rep 2023; 13:8421. [PMID: 37225782 DOI: 10.1038/s41598-023-35333-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/16/2023] [Indexed: 05/26/2023] Open
Abstract
Early prediction of neonates' survival and Length of Stay (LOS) in Neonatal Intensive Care Units (NICU) is effective in decision-making. We developed an intelligent system to predict neonatal survival and LOS using the "Case-Based Reasoning" (CBR) method. We developed a web-based CBR system based on K-Nearest Neighborhood (KNN) on 1682 neonates and 17 variables for mortality and 13 variables for LOS and evaluated the system with 336 retrospectively collected data. We implemented the system in a NICU to externally validate the system and evaluate the system prediction acceptability and usability. Our internal validation on the balanced case base showed high accuracy (97.02%), and F-score (0.984) for survival prediction. The root Mean Square Error (RMSE) for LOS was 4.78 days. External validation on the balanced case base indicated high accuracy (98.91%), and F-score (0.993) to predict survival. RMSE for LOS was 3.27 days. Usability evaluation showed that more than half of the issues identified were related to appearance and rated as a low priority to be fixed. Acceptability assessment showed a high acceptance and confidence in responses. The usability score (80.71) indicated high system usability for neonatologists. This system is available at http://neonatalcdss.ir/ . Positive results of our system in terms of performance, acceptability, and usability indicated this system can be used to improve neonatal care.
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Affiliation(s)
- Farzaneh Kermani
- Health Information Technology Department, School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran
| | - Mohammad Reza Zarkesh
- Maternal, Fetal and Neonatal Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neonatology, Yas Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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El-Sappagh S, Alonso-Moral JM, Abuhmed T, Ali F, Bugarín-Diz A. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10415-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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Sotoudeh-Anvari M, Sotoudeh-Anvari A. Setback in ranking fuzzy numbers: a study in fuzzy risk analysis in diabetes prediction. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10282-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Riali I, Fareh M, Ibnaissa MC, Bellil M. A semantic-based approach for hepatitis C virus prediction and diagnosis using a fuzzy ontology and a fuzzy Bayesian network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Medical decisions, especially when diagnosing Hepatitis C, are challenging to make as they often have to be based on uncertain and fuzzy information. In most cases, that puts doctors in complex yet uncertain decision-making situations. Therefore, it would be more suitable for doctors to use a semantically intelligent system that mimics the doctor’s thinking and enables fast Hepatitis C diagnosis. Fuzzy ontologies have been used to remedy the shortcomings of classical ontologies by using fuzzy logic, which allows dealing with fuzzy knowledge in ontologies. Moreover, Fuzzy Bayesian networks are well-known and widely used to represent and analyze uncertain medical data. This paper presents a system that combines fuzzy ontologies and Bayesian networks to diagnose Hepatitis C. The system uses a fuzzy ontology to represent sequences of uncertain and fuzzy data about patients and some features relevant to Hepatitis C diagnosis, enabling more reusable and interpretable datasets. In addition, we propose a novel semantic diagnosis process based on a fuzzy Bayesian network as an inference engine. We conducted an experimental study on 615 real cases to validate the proposed system. The experimentation allowed us to compare the results of existing machine learning algorithms for the Hepatitis C diagnosis with the results of our proposed system. Our solution shows promising results and proves effective for fast medical assistance.
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Affiliation(s)
- Ishak Riali
- LRDSI Laboratory, Faculty of Sciences, University of Blida 1, Algeria
| | - Messaouda Fareh
- LRDSI Laboratory, Faculty of Sciences, University of Blida 1, Algeria
| | | | - Mounir Bellil
- LRDSI Laboratory, Faculty of Sciences, University of Blida 1, Algeria
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Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12030950] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is that diabetes datasets are limited and contain outliers and missing data. Additionally, there is a trade-off between classification accuracy and operational law for detecting diabetes. In this paper, an algorithm for diabetes classification is proposed for pregnant women using the Pima Indians Diabetes Dataset (PIDD). First, a preprocessing step in the proposed algorithm includes outlier rejection, imputing missing values, the standardization process, and feature selection of the attributes, which enhance the dataset’s quality. Second, the classifier uses the fuzzy KNN method and modifies the membership function based on the uncertainty theory. Third, a grid search method is applied to achieve the best values for tuning the fuzzy KNN method based on uncertainty membership, as there are hyperparameters that affect the performance of the proposed classifier. In turn, the proposed tuned fuzzy KNN based on uncertainty classifiers (TFKNN) deals with the belief degree, handles membership functions and operation law, and avoids making the wrong categorization. The proposed algorithm performs better than other classifiers that have been trained and evaluated, including KNN, fuzzy KNN, naïve Bayes (NB), and decision tree (DT). The results of different classifiers in an ensemble could significantly improve classification precision. The TFKNN has time complexity O(kn2d), and space complexity O(n2d). The TFKNN model has high performance and outperformed the others in all tests in terms of accuracy, specificity, precision, and average AUC, with values of 90.63, 85.00, 93.18, and 94.13, respectively. Additionally, results of empirical analysis of TFKNN compared to fuzzy KNN, KNN, NB, and DT demonstrate the global superiority of TFKNN in precision, accuracy, and specificity.
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Xie Y, Gu D, Wang X, Yang X, Zhao W, Khakimova AK, Liu H. A Smart Healthcare Knowledge Service Framework for Hierarchical Medical Treatment System. Healthcare (Basel) 2021; 10:healthcare10010032. [PMID: 35052196 PMCID: PMC8774779 DOI: 10.3390/healthcare10010032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/07/2021] [Accepted: 12/20/2021] [Indexed: 11/19/2022] Open
Abstract
This paper reveals the research hotspots and development directions of case-based reasoning in the field of health care, and proposes the framework and key technologies of medical knowledge service systems based on case-based reasoning (CBR) in the big data environment. The 2124 articles on medical CBR in the Web of Science were visualized and analyzed using a bibliometrics method, and a CBR-based knowledge service system framework was constructed in the medical Internet of all people, things and data resources environment. An intelligent construction method for the clinical medical case base and the gray case knowledge reasoning model were proposed. A cloud-edge collaboration knowledge service system was developed and applied in a pilot project. Compared with other diagnostic tools, the system provides case-based explanations for its predicted results, making it easier for physicians to understand and accept, so that they can make better decisions. The results show that the system has good interpretability, has better acceptance than the common intelligent decision support system, and strongly supports physician auxiliary diagnosis and treatment as well as clinical teaching.
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Affiliation(s)
- Yi Xie
- The School of Management, Hefei University of Technology, Hefei 230009, China; (Y.X.); (W.Z.); (H.L.)
- The School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK
| | - Dongxiao Gu
- The School of Management, Hefei University of Technology, Hefei 230009, China; (Y.X.); (W.Z.); (H.L.)
- Correspondence: (D.G.); (X.Y.)
| | - Xiaoyu Wang
- The Department of Pharmacy, Anhui University of Traditional Chinese Medicine, Hefei 230009, China;
| | - Xuejie Yang
- The School of Management, Hefei University of Technology, Hefei 230009, China; (Y.X.); (W.Z.); (H.L.)
- Correspondence: (D.G.); (X.Y.)
| | - Wang Zhao
- The School of Management, Hefei University of Technology, Hefei 230009, China; (Y.X.); (W.Z.); (H.L.)
| | - Aida K. Khakimova
- Scientific-Research Center for Physical-Technical Informatics, Russian New University, 105005 Moscow, Russia;
| | - Hu Liu
- The School of Management, Hefei University of Technology, Hefei 230009, China; (Y.X.); (W.Z.); (H.L.)
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Remote Diagnosis System of Uremia Complicated with Sleep Disorder and Effectiveness of Nursing Intervention. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:4649139. [PMID: 34867112 PMCID: PMC8610649 DOI: 10.1155/2021/4649139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 10/26/2021] [Indexed: 11/24/2022]
Abstract
This study aimed to analyze the related factors and remote diagnosis of sleep disorders in patients with uremia and to provide the basis for clinical intervention measures. The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate the sleep quality of 100 patients with uremia. The experimental results show that among 100 patients with uremia, 83 patients (83%) were complicated with sleep disorder, and the average PSQI score was (10.73 ± 1.36). The PSQI score was significantly higher in the elderly, patients with long duration of dialysis, patients with elevated CRP, patients with substandard anemia, and patients with substandard calcium and phosphorus, with statistical significance (P < 0.05). The sleep quality of patients with uremia is correlated with depression, age, and penetration age. The worse the sleep quality is, the more severe the depression is. It proved that the sleep quality of dialysis patients is an important factor. Conclusion. Sleep disorder is a common complication in patients with uremia. Age, anemia, calcium and phosphorus abnormality, permeation age, and inflammatory status are important factors affecting sleep quality in dialysis patients.
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Reyes-Peña C, Tovar M, Bravo M, Motz R. An ontology network for Diabetes Mellitus in Mexico. J Biomed Semantics 2021; 12:19. [PMID: 34625104 PMCID: PMC8500829 DOI: 10.1186/s13326-021-00252-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/14/2021] [Indexed: 12/04/2022] Open
Abstract
Background Medical experts in the domain of Diabetes Mellitus (DM) acquire specific knowledge from diabetic patients through monitoring and interaction. This allows them to know the disease and information about other conditions or comorbidities, treatments, and typical consequences of the Mexican population. This indicates that an expert in a domain knows technical information about the domain and contextual factors that interact with it in the real world, contributing to new knowledge generation. For capturing and managing information about the DM, it is necessary to design and implement techniques and methods that allow: determining the most relevant conceptual dimensions and their correct organization, the integration of existing medical and clinical information from different resources, and the generation of structures that represent the deduction process of the doctor. An Ontology Network is a collection of ontologies of diverse knowledge domains which can be interconnected by meta-relations. This article describes an Ontology Network for representing DM in Mexico, designed by a proposed methodology. The information used for Ontology Network building include the ontological resource reuse and non-ontological resource transformation for ontology design and ontology extending by natural language processing techniques. These are medical information extracted from vocabularies, taxonomies, medical dictionaries, ontologies, among others. Additionally, a set of semantic rules has been defined within the Ontology Network to derive new knowledge. Results An Ontology Network for DM in Mexico has been built from six well-defined domains, resulting in new classes, using ontological and non-ontological resources to offer a semantic structure for assisting in the medical diagnosis process. The network comprises 1367 classes, 20 object properties, 63 data properties, and 4268 individuals from seven different ontologies. Ontology Network evaluation was carried out by verifying the purpose for its design and some quality criteria. Conclusions The composition of the Ontology Network offers a set of well-defined ontological modules facilitating the reuse of one or more of them. The inclusion of international vocabularies as SNOMED CT or ICD-10 reinforces the representation by international standards. It increases the semantic interoperability of the network, providing the opportunity to integrate other ontologies with the same vocabularies. The ontology network design methodology offers a guide for ontology developers about how to use ontological and non-ontological resources in order to exploit the maximum of information and knowledge from a set of domains that share or not information.
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Affiliation(s)
- Cecilia Reyes-Peña
- Faculty of Computer Science, Benemerita Universidad Autonoma de Puebla, Av. San Claudio, Puebla, Mexico.
| | - Mireya Tovar
- Faculty of Computer Science, Benemerita Universidad Autonoma de Puebla, Av. San Claudio, Puebla, Mexico
| | - Maricela Bravo
- Universidad Autonoma Metropolitana, Av. San Pablo No. 180, Mexico City, Mexico
| | - Regina Motz
- Universidad de la Republica, Julio Herrera y Reissig 565, Montevideo, Uruguay
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Oyelade ON, Ezugwu AE, Adewuyi SA. Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies. Neural Comput Appl 2021; 34:3053-3078. [PMID: 34642549 PMCID: PMC8500271 DOI: 10.1007/s00521-021-06517-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 09/07/2021] [Indexed: 01/26/2023]
Abstract
The need to address the challenge of vagueness across several domains of applicability of ontology is gaining research attention. The presence of vagueness in knowledge represented with description logic impairs automating reasoning and inference making. The importance of reducing this vagueness in the formalization of medical knowledge representation is rising, considering the vulnerability of this domain to the expression of vague concepts or terms. This vagueness may be addressed from the perspective of ontology modeling language application such as ontology web language (OWL). Although several attempts have been made to tackle this problem in other disease prognoses such as diabetes and cardiovascular diseases, a similar effort is missing for breast cancer. Minimizing vagueness in breast cancer ontology is necessary to enhance automated reasoning and handle knowledge representation problems. This study proposes a framework for reducing vagueness in breast cancer ontology. The approach obtained breast cancer crisp ontology and applied fuzzy ontology elements based on the Fuzzy OWL2 model to formulate breast cancer fuzzy ontology. This was achieved by extending the elements of OWL2 (a more expressive version of OWL) with annotation properties to fuzzify the breast cancer crisp ontology. Results obtained showed a significant reduction of vagueness in the domain, yielding 0.38 for vagueness spread and 1.0 for vagueness explicitness. In addition, ontology metrics such as completeness, consistency, correctness and accuracy were also evaluated, and we obtained impressive performance. The implication of this result is the reduction of vagueness in breast cancer ontology, which provides increased computational reasoning support to applications using the ontology.
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Affiliation(s)
- Olaide N. Oyelade
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Sunday A. Adewuyi
- Department of Radiotherapy and Oncology, Ahmadu Bello University, Zaria, Nigeria
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Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier. Comput Biol Med 2021; 136:104664. [PMID: 34329866 DOI: 10.1016/j.compbiomed.2021.104664] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 07/18/2021] [Accepted: 07/18/2021] [Indexed: 11/20/2022]
Abstract
In this work, a non-invasive diabetes mellitus detection system is proposed based on the wristband photoplethysmography (PPG) signal and basic physiological parameters (PhyP) to enable easy detection of diabetes mellitus (DM). A dataset of 217 participants with diabetes, prediabetes and normal conditions is used to develop the system. The Mel frequency cepstral coefficients (MFCC) extracted from 5s PPG signal segments and the PhyP are used as input for the machine learning algorithms. The K-nearest neighbors, support vector machine, random forest and extreme gradient boost (XGBoost) classifiers are used for classification. In addition, a hybrid feature selection method (Hybrid FS) is proposed to reduce the size of the input data. The Hybrid FS-based XGBoost system achieves a high accuracy of 99.93 % for non-invasive diabetes detection with fewer features and less computational effort. The analysis suggests that the PPG signal from a wearable sensor is a good alternative for simple non-invasive blood glucose measurements in routine applications.
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Bakırarar B, Yüksel C, Yavuz Y. Prediction of new prescription requirements for diabetes patients using big data technologies. JOURNAL OF HEALTH RESEARCH 2021. [DOI: 10.1108/jhr-05-2020-0136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
PurposeThe study aimed to evaluate the effectiveness of using large data sets for new diabetes patient prescriptions.Design/methodology/approachThis study consisted of 101,766 individuals, who had applied to the hospital with a diabetes diagnosis and were hospitalized for 1–14 days and subjected to laboratory tests and medication.FindingsWith the help of Mahout and Scala, data mining methods of random forest and multilayer perceptron were used. Accuracy rates of these methods were found to be 0.879 and 0.849 for Mahout and 0.849 and 0.870 for Scala.Originality/valueThe mahout random forest method provided a better prediction of new prescription requirements than the other methods according to accuracy criteria.
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Chen T, Shang C, Su P, Keravnou-Papailiou E, Zhao Y, Antoniou G, Shen Q. A Decision Tree-Initialised Neuro-fuzzy Approach for Clinical Decision Support. Artif Intell Med 2020; 111:101986. [PMID: 33461686 DOI: 10.1016/j.artmed.2020.101986] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 08/23/2020] [Accepted: 11/03/2020] [Indexed: 10/23/2022]
Abstract
Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand the deployment of interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based models are generally considered interpretable that are able to reflect the associations between medical conditions and associated symptoms, through the use of linguistic if-then statements. Systems built on top of fuzzy sets are of particular appealing to medical applications since they enable the tolerance of vague and imprecise concepts that are often embedded in medical entities such as symptom description and test results. They facilitate an approximate reasoning framework which mimics human reasoning and supports the linguistic delivery of medical expertise often expressed in statements such as 'weight low' or 'glucose level high' while describing symptoms. This paper proposes an approach by performing data-driven learning of accurate and interpretable fuzzy rule bases for clinical decision support. The approach starts with the generation of a crisp rule base through a decision tree learning mechanism, capable of capturing simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the framework of adaptive network-based fuzzy inference system (ANFIS), thereby further optimising the parameters of both rule antecedents and consequents. Experimental studies on popular medical data benchmarks demonstrate that the proposed work is able to learn compact rule bases involving simple rule antecedents, with statistically better or comparable performance to those achieved by state-of-the-art fuzzy classifiers.
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Affiliation(s)
- Tianhua Chen
- Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK.
| | - Changjing Shang
- Department of Computer Science, Faculty of Business and Physical Science, Aberystwyth University, Aberystwyth, UK
| | - Pan Su
- Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences, China; School of Control and Computer Engineering, North China Electric Power University, Baoding, China
| | | | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences, China
| | - Grigoris Antoniou
- Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | - Qiang Shen
- Department of Computer Science, Faculty of Business and Physical Science, Aberystwyth University, Aberystwyth, UK
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Feuillâtre H, Auffret V, Castro M, Lalys F, Le Breton H, Garreau M, Haigron P. Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation. PLoS One 2020; 15:e0238463. [PMID: 32881919 PMCID: PMC7470320 DOI: 10.1371/journal.pone.0238463] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/16/2020] [Indexed: 11/18/2022] Open
Abstract
In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps-retrieve, reuse, revise, and retain. The proposed case-based reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve- and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-of-the-art similarity measures.
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Affiliation(s)
- Hélène Feuillâtre
- Univ Rennes, CHU Rennes, Inserm, LTSI–UMR 1099, Rennes, France
- * E-mail:
| | - Vincent Auffret
- Univ Rennes, CHU Rennes, Inserm, LTSI–UMR 1099, Rennes, France
| | - Miguel Castro
- Univ Rennes, CHU Rennes, Inserm, LTSI–UMR 1099, Rennes, France
| | | | - Hervé Le Breton
- Univ Rennes, CHU Rennes, Inserm, LTSI–UMR 1099, Rennes, France
| | | | - Pascal Haigron
- Univ Rennes, CHU Rennes, Inserm, LTSI–UMR 1099, Rennes, France
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17
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Kiyasseh D, Zhu T, Clifton D. The Promise of Clinical Decision Support Systems Targetting Low-Resource Settings. IEEE Rev Biomed Eng 2020; 15:354-371. [PMID: 32813662 DOI: 10.1109/rbme.2020.3017868] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage of high-quality medical expertise and infrastructure. Together, these phenomena lead to over-burdened healthcare systems that under-serve the needs of the community. Alleviating this burden can be undertaken by the introduction of clinical decision support systems (CDSSs); systems that support stakeholders (ranging from physicians to patients) within the clinical setting in their day-to-day activities. Such systems, which have proven to be effective in the developed world, remain to be under-explored in low-resource settings. This review attempts to summarize the research focused on clinical decision support systems that either target stakeholders within low-resource clinical settings or diseases commonly found in such environments. When categorizing our findings according to disease applications, we find that CDSSs are predominantly focused on dealing with bacterial infections and maternal care, do not leverage deep learning, and have not been evaluated prospectively. Together, these highlight the need for increased research in this domain in order to impact a diverse set of medical conditions and ultimately improve patient outcomes.
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18
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Oyelade ON, Ezugwu AE. A case-based reasoning framework for early detection and diagnosis of novel coronavirus. INFORMATICS IN MEDICINE UNLOCKED 2020; 20:100395. [PMID: 32835080 PMCID: PMC7377815 DOI: 10.1016/j.imu.2020.100395] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/06/2020] [Accepted: 07/09/2020] [Indexed: 12/29/2022] Open
Abstract
Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 11,301,850 confirmed cases while more than 531,806 have died due to it, with these figures rising daily across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is, therefore, crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is a compelling paradigm that allows for the utilization of case-specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study, therefore, aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in the classification of suspected cases of COVID-19. The CBR model leverages on a novel feature selection and the semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved from 71 (67 adults and 4 pediatrics) cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories with an accuracy of 94.54%. The study found that the proposed model can support physicians to easily diagnose suspected cases of COVID-19 based on their medical records without subjecting the specimen to laboratory tests. As a result, there will be a global minimization of contagion rate occasioned by slow testing and in addition, reduced false-positive rates of diagnosed cases as observed in some parts of the globe.
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Affiliation(s)
- Olaide N Oyelade
- Department of Computer Science, Ahmadu Bello University Zaria, Nigeria
- School of Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Absalom E Ezugwu
- School of Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
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19
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Srivastava AK, Kumar Y, Singh PK. A Rule-Based Monitoring System for Accurate Prediction of Diabetes. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2020. [DOI: 10.4018/ijehmc.2020070103] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Diabetes is a chronic disease that can affect the life of people due to high sugar level in their blood. The sugar level is increased due to a lack of production of insulin in the human body. Large numbers of people are affected with diabetes and it can increase tremendously due life style behavior. Diabetes can also affect the other human organs, like kidneys, hearts, retinas and lead to the failure of these organs. This article presents a diabetic monitoring system to determine the risk of diabetes based on the personal health record of patients. In this work, several rules are designed based on the clinical as well as non-clinical symptoms. The effectiveness of the diabetes monitoring system is tested on a set of two hundred forty people. The simulation results are also compared with well-known techniques available for diabetes prediction. It is stated that proposed monitoring system obtains 90.41% accuracy rate as compared with other techniques.
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Affiliation(s)
| | - Yugal Kumar
- Jaypee University of Information Technology, India
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20
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Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2020. [DOI: 10.1016/j.jksuci.2020.06.013] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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21
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Zhai Z, Martínez Ortega JF, Beltran V, Lucas Martínez N. An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval. SENSORS (BASEL, SWITZERLAND) 2019; 19:s19235118. [PMID: 31766763 PMCID: PMC6929128 DOI: 10.3390/s19235118] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/13/2019] [Accepted: 11/20/2019] [Indexed: 06/10/2023]
Abstract
As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value pair, and ontological representations have been well explored by researchers. However, these methods may lead to inefficient case retrieval when a large volume of data is stored in the case base. Thus, an associated representation method is proposed in this paper for fast case retrieval. Each case is interconnected with several similar and dissimilar ones. Once a new case is reported, its features are compared with historical data by similarity measurements for identifying a relative similar past case. The similarity of associated cases is measured preferentially, instead of comparing all the cases in the case base. Experiments on case retrieval were performed between the associated case representation and traditional methods, following two criteria: the number of visited cases and retrieval accuracy. The result demonstrates that our proposal enables fast case retrieval with promising accuracy by visiting fewer past cases. In conclusion, the associated case representation method outperforms traditional methods in the aspect of retrieval efficiency.
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22
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Nasiri S, Zahedi G, Kuntz S, Fathi M. Knowledge representation and management based on an ontological CBR system for dementia caregiving. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.027] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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23
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A Fibrosis Diagnosis Clinical Decision Support System Using Fuzzy Knowledge. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018. [DOI: 10.1007/s13369-018-3670-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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24
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Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Sepehri MM, Azaron A. Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment. Artif Intell Med 2018; 87:20-33. [PMID: 29606521 DOI: 10.1016/j.artmed.2018.03.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 03/07/2018] [Accepted: 03/13/2018] [Indexed: 12/22/2022]
Abstract
Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed to control the patient's blood glucose. The wide range of hyperglycemia lowering agents with varying effects and various side effects makes the decision quite difficult. This paper presents computer-aided medical decision support using a fuzzy Multi-Criteria Decision-Making (MCDM) model that hybridizes a Step-wise Weight Assessment Ratio Analysis (SWARA) method with a modification of Fuzzy Multi-Objective Optimization on the basis of a Ratio Analysis plus the full multiplicative form (FMULTIMOORA) method for pharmacological therapy selection of T2D. It makes the use of SWARA for obtaining the relative significance of every selected criterion by soliciting experts' opinions and FMULTIMOORA method for evaluation of each alternative according to all criteria based on a published clinical guideline. In this paper, an extended reference point approach is considered in the proposed hybrid MCDM model that resolves the classic reference point limitations and improves the FMULTIMOORA ranking procedure. Computational results indicate that Metformin is confirmed as the first-line medication and Sulfonylurea as the second-line add-on therapy. The Glucagon-like peptide-1 receptor agonist, Dipeptidyl peptidase-4 inhibitor, and Insulin are placed 3rd, 4th, and 5th, respectively. A sensitivity analysis is conducted to validate the model performance by comparing its result with studies in the literature, other fuzzy MCDM techniques and an interval MULTIMOORA method based on an observational dataset. The close correspondence between the final rankings of anti-diabetic agents resulted from the proposed hybrid model and other methodologies provide significant implications for endocrinologists to refer.
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Affiliation(s)
- M Eghbali-Zarch
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - R Tavakkoli-Moghaddam
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran; LCFC, Arts et Métiers Paris Tech, Metz, France; Universal Scientific Education and Research Network (USERN), Tehran, Iran.
| | - F Esfahanian
- Department of Endocrinology & Metabolism, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M M Sepehri
- Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - A Azaron
- Beedie School of Business, Simon Fraser University, Vancouver, Canada; School of Business, Kwantlen Polytechnic University, Vancouver, Canada
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25
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Rigla M, García-Sáez G, Pons B, Hernando ME. Artificial Intelligence Methodologies and Their Application to Diabetes. J Diabetes Sci Technol 2018; 12:303-310. [PMID: 28539087 PMCID: PMC5851211 DOI: 10.1177/1932296817710475] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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.
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Affiliation(s)
- Mercedes Rigla
- Endocrinology and Nutrition Department, Parc Tauli University Hospital, Sabadell, Spain
- Mercedes Rigla, MD, PhD, Endocrinology and Nutrition Department, Parc Tauli University Hospital, I3PT, Autonomous University of Barcelona, Parc Taulí, 1, Sabadell, 08208, Spain.
| | - Gema García-Sáez
- Bioengineering and Telemedicine Centre, Universidad Politécnica de Madrid, Spain
- CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
| | - Belén Pons
- Endocrinology and Nutrition Department, Parc Tauli University Hospital, Sabadell, Spain
| | - Maria Elena Hernando
- Bioengineering and Telemedicine Centre, Universidad Politécnica de Madrid, Spain
- CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
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26
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Mezei J, Nikou S. Fuzzy optimization to improve mobile health and wellness recommendation systems. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2017.11.030] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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27
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Siddiqui SA, Zhang Y, Lloret J, Song H, Obradovic Z. Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects. IEEE Rev Biomed Eng 2018; 11:21-35. [DOI: 10.1109/rbme.2018.2822301] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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28
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Gu D, Liang C, Zhao H. A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis. Artif Intell Med 2017; 77:31-47. [PMID: 28545610 DOI: 10.1016/j.artmed.2017.02.003] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Revised: 01/20/2017] [Accepted: 02/05/2017] [Indexed: 11/24/2022]
Abstract
OBJECTIVE We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. METHODS We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts. We therefore used 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. We evaluated our CBR system in two case studies, related to benign/malignant tumor prediction and secondary cancer prediction, respectively. RESULT Weighted heterogeneous value distance metric with genetic algorithm for weight learning outperformed several alternative attribute matching methods and several classification methods by at least 3.4%, reaching 0.938, 0.883, 0.933, and 0.984 in the first case study, and 0.927, 0.842, 0.939, and 0.989 in the second case study, in terms of accuracy, sensitivity×specificity, F measure, and area under the receiver operating characteristic curve, respectively. CONCLUSION The evaluation result indicates the potential of CBR in the breast cancer diagnosis domain.
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Affiliation(s)
- Dongxiao Gu
- School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui, 230009, China.
| | - Changyong Liang
- School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui, 230009, China.
| | - Huimin Zhao
- Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, 3202 North Maryland Avenue, Milwaukee, WI, 53201, USA.
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29
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Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine Learning and Data Mining Methods in Diabetes Research. Comput Struct Biotechnol J 2017; 15:104-116. [PMID: 28138367 PMCID: PMC5257026 DOI: 10.1016/j.csbj.2016.12.005] [Citation(s) in RCA: 340] [Impact Index Per Article: 48.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 12/20/2016] [Accepted: 12/27/2016] [Indexed: 12/14/2022] Open
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.
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Affiliation(s)
- Ioannis Kavakiotis
- Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Institute of Applied Biosciences, CERTH, Thessaloniki, Greece
| | - Olga Tsave
- Laboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Athanasios Salifoglou
- Laboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Nicos Maglaveras
- Institute of Applied Biosciences, CERTH, Thessaloniki, Greece
- Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Ioannis Vlahavas
- Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Ioanna Chouvarda
- Institute of Applied Biosciences, CERTH, Thessaloniki, Greece
- Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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30
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Ontology Based Possibilistic Reasoning for Breast Cancer Aided Diagnosis. INFORM SYST 2017. [DOI: 10.1007/978-3-319-65930-5_29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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31
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Vilhena J, Vicente H, Martins MR, Grañeda JM, Caldeira F, Gusmão R, Neves J, Neves J. A case-based reasoning view of thrombophilia risk. J Biomed Inform 2016; 62:265-75. [PMID: 27404848 DOI: 10.1016/j.jbi.2016.07.013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 07/07/2016] [Accepted: 07/08/2016] [Indexed: 11/28/2022]
Abstract
Thrombophilia stands for a genetic or an acquired tendency to hypercoagulable states that increase the risk of venous and arterial thromboses. Indeed, venous thromboembolism is often a chronic illness, mainly in deep venous thrombosis and pulmonary embolism, requiring lifelong prevention strategies. Therefore, it is crucial to identify the cause of the disease, the most appropriate treatment, the length of treatment or prevent a thrombotic recurrence. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a logic programming approach to knowledge representation and reasoning, complemented with a case-based approach to computing. The proposed model has been quite accurate in the assessment of thrombophilia predisposition risk, since the overall accuracy is higher than 90% and sensitivity ranging in the interval [86.5%, 88.1%]. The main strength of the proposed solution is the ability to deal explicitly with incomplete, unknown, or even self-contradictory information.
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Affiliation(s)
- João Vilhena
- Departamento de Química, Escola de Ciências e Tecnologia, Universidade de Évora, Évora, Portugal.
| | - Henrique Vicente
- Departamento de Química, Escola de Ciências e Tecnologia, Universidade de Évora, Évora, Portugal; Centro Algoritmi, Universidade do Minho, Braga, Portugal.
| | - M Rosário Martins
- Departamento de Química, Escola de Ciências e Tecnologia, Laboratório HERCULES, Universidade de Évora, Évora, Portugal.
| | - José M Grañeda
- Serviço de Patologia Clínica do Hospital do Espírito Santo de Évora EPE, Évora, Portugal.
| | - Filomena Caldeira
- Serviço de Patologia Clínica do Hospital do Espírito Santo de Évora EPE, Évora, Portugal.
| | - Rodrigo Gusmão
- Serviço de Patologia Clínica do Hospital do Espírito Santo de Évora EPE, Évora, Portugal.
| | - João Neves
- Drs. Nicolas & Asp, Dubai, United Arab Emirates.
| | - José Neves
- Centro Algoritmi, Universidade do Minho, Braga, Portugal.
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32
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Martin A, Emmenegger S, Hinkelmann K, Thönssen B. A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management. ENTERP INF SYST-UK 2016. [DOI: 10.1080/17517575.2016.1161239] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Andreas Martin
- School of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Olten, Switzerland
- School of Computing, University of South Africa, Florida Park, Roodepoort, Johannesburg, South Africa
| | - Sandro Emmenegger
- School of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Olten, Switzerland
| | - Knut Hinkelmann
- School of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Olten, Switzerland
- Department of Informatics, University of Pretoria, Pretoria, South Africa
| | - Barbara Thönssen
- School of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Olten, Switzerland
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