Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems
ObjectivesSmartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in p...
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2017-10-01
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| Series: | Healthcare Informatics Research |
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| Online Access: | http://e-hir.org/upload/pdf/hir-23-262.pdf |
| _version_ | 1798025690821427200 |
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| author | Mina Fallah Sharareh R. Niakan Kalhori |
| author_facet | Mina Fallah Sharareh R. Niakan Kalhori |
| author_sort | Mina Fallah |
| collection | DOAJ |
| description | ObjectivesSmartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems.MethodsWe systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed.ResultsData mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management.ConclusionsEmbedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output. |
| first_indexed | 2024-04-11T18:22:54Z |
| format | Article |
| id | doaj.art-2bf34ac652dd4bc68ed214208d0e64f1 |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-04-11T18:22:54Z |
| publishDate | 2017-10-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-2bf34ac652dd4bc68ed214208d0e64f12022-12-22T04:09:43ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2017-10-0123426227010.4258/hir.2017.23.4.262869Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information SystemsMina Fallah0Sharareh R. Niakan Kalhori1Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.ObjectivesSmartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems.MethodsWe systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed.ResultsData mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management.ConclusionsEmbedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.http://e-hir.org/upload/pdf/hir-23-262.pdfdata miningpatient caremobile healthinformation systemartificial intelligence |
| spellingShingle | Mina Fallah Sharareh R. Niakan Kalhori Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems Healthcare Informatics Research data mining patient care mobile health information system artificial intelligence |
| title | Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems |
| title_full | Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems |
| title_fullStr | Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems |
| title_full_unstemmed | Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems |
| title_short | Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems |
| title_sort | systematic review of data mining applications in patient centered mobile based information systems |
| topic | data mining patient care mobile health information system artificial intelligence |
| url | http://e-hir.org/upload/pdf/hir-23-262.pdf |
| work_keys_str_mv | AT minafallah systematicreviewofdataminingapplicationsinpatientcenteredmobilebasedinformationsystems AT shararehrniakankalhori systematicreviewofdataminingapplicationsinpatientcenteredmobilebasedinformationsystems |