Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems / 대한의료정보학회지
Healthcare Informatics Research
;
: 262-270, 2017.
Article
in English
| WPRIM
| ID: wpr-195863
ABSTRACT
OBJECTIVES:
Smartphones 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.METHODS:
We 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.RESULTS:
Data mining techniques have been reported in development of mobile health apps for three mainpurposes:
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.CONCLUSIONS:
Embedded 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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Self Care
/
Information Systems
/
Artificial Intelligence
/
Decision Trees
/
Mass Screening
/
Data Collection
/
Statistics as Topic
/
Follow-Up Studies
/
Telemedicine
/
Fuzzy Logic
Type of study:
Diagnostic study
/
Practice guideline
/
Observational study
/
Prognostic study
/
Screening study
/
Systematic reviews
Limits:
Humans
Language:
English
Journal:
Healthcare Informatics Research
Year:
2017
Type:
Article
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