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Casal-Guisande M, Comesaña-Campos A, Dutra I, Cerqueiro-Pequeño J, Bouza-Rodríguez JB. Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk. J Pers Med 2022; 12:jpm12020169. [PMID: 35207657 PMCID: PMC8880667 DOI: 10.3390/jpm12020169] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 12/24/2022] Open
Abstract
Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.
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Affiliation(s)
- Manuel Casal-Guisande
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain; (J.C.-P.); (J.-B.B.-R.)
- Department of Computer Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal;
- Center for Health Technologies and Information Systems Research–CINTESIS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
- Correspondence: (M.C.-G.); (A.C.-C.)
| | - Alberto Comesaña-Campos
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain; (J.C.-P.); (J.-B.B.-R.)
- Center for Health Technologies and Information Systems Research–CINTESIS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
- Correspondence: (M.C.-G.); (A.C.-C.)
| | - Inês Dutra
- Department of Computer Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal;
- Center for Health Technologies and Information Systems Research–CINTESIS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
| | - Jorge Cerqueiro-Pequeño
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain; (J.C.-P.); (J.-B.B.-R.)
- Center for Health Technologies and Information Systems Research–CINTESIS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
| | - José-Benito Bouza-Rodríguez
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain; (J.C.-P.); (J.-B.B.-R.)
- Center for Health Technologies and Information Systems Research–CINTESIS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
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Zolhavarieh S, Parry D, Bai Q. Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature. JMIR Med Inform 2017; 5:e18. [PMID: 28679487 PMCID: PMC5517823 DOI: 10.2196/medinform.6169] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 12/19/2016] [Accepted: 03/28/2017] [Indexed: 11/17/2022] Open
Abstract
Background Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources. Objective The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS. Methods We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers. Results The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study. Conclusions The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.
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Affiliation(s)
- Seyedjamal Zolhavarieh
- Department of Computer Science, Auckland University of Technology, Auckland, New Zealand
| | - David Parry
- Department of Computer Science, Auckland University of Technology, Auckland, New Zealand
| | - Quan Bai
- Department of Computer Science, Auckland University of Technology, Auckland, New Zealand
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Nguyen NT, Nguyen VD, Hwang D. An influence analysis of the number of members on the quality of knowledge in a collective. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-169121] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Ngoc Thanh Nguyen
- Division of Knowledge and System Engineering for ICT, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland
| | - Van Du Nguyen
- Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland
- Department of Computer Engineering, Yeungnam University, Korea
| | - Dosam Hwang
- Department of Computer Engineering, Yeungnam University, Korea
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Isern D, Moreno A. A Systematic Literature Review of Agents Applied in Healthcare. J Med Syst 2015; 40:43. [PMID: 26590981 DOI: 10.1007/s10916-015-0376-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 10/09/2015] [Indexed: 12/26/2022]
Abstract
Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions. A review of the most recent literature (2009-2014) of applications of agents in healthcare is discussed, and two classifications considering the main goal of the health systems as well as the main actors involved have been investigated. This review shows that the number of published works exhibits a growing interest of researchers in this field in a wide range of applications.
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Affiliation(s)
- David Isern
- Department of Computer Science and Mathematics, ITAKA Research Group, Universitat Rovira i Virgili, Avda. Països Catalans, 26, 43007, Tarragona, Catalonia (Spain).
| | - Antonio Moreno
- Department of Computer Science and Mathematics, ITAKA Research Group, Universitat Rovira i Virgili, Avda. Països Catalans, 26, 43007, Tarragona, Catalonia (Spain).
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Bioinspired and knowledge based techniques and applications. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.09.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Sanchez E, Peng W, Toro C, Sanin C, Graña M, Szczerbicki E, Carrasco E, Guijarro F, Brualla L. Decisional DNA for modeling and reuse of experiential clinical assessments in breast cancer diagnosis and treatment. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.06.032] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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