Healthc Inform Res.  2017 Oct;23(4):262-270. 10.4258/hir.2017.23.4.262.

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

• Fallah M
• Niakan Kalhori SR

• KMID: 2396417
• DOI: http://doi.org/10.4258/hir.2017.23.4.262

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 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.
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.

Keyword

Data Mining; Patient Care; Mobile Health; Information System; Artificial Intelligence

MeSH Terms

Artificial Intelligence
Data Collection
Data Mining*
Decision Trees
Delivery of Health Care
Early Diagnosis
Education
Follow-Up Studies
Fuzzy Logic
Humans
Information Systems*
Mass Screening
Methods
Mobile Applications
Patient Care
Self Care
Smartphone
Statistics as Topic
Support Vector Machine
Telemedicine

Figure

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  Figure 1 Process of PRISMA for data collection and analysis.

• 

  Figure 2 Distribution of illnesses covered by mobile applications improved by various data mining methods.

• 

  Figure 3 Frequency of data mining methods enhancing mobile application mainly used for patient self-management.

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