Applying sequential pattern mining to investigate cerebrovascular health outpatients' re-visit patterns

Chao Ou-Yang; Chandrawati Putri Wulandari; Rizka Aisha Rahmi Hariadi; Han-Cheng Wang; Chiehfeng Chen

doi:10.7717/peerj.5183

Applying sequential pattern mining to investigate cerebrovascular health outpatients' re-visit patterns

PeerJ. 2018 Jul 9:6:e5183. doi: 10.7717/peerj.5183. eCollection 2018.

Authors

Chao Ou-Yang¹, Chandrawati Putri Wulandari^{1

2}, Rizka Aisha Rahmi Hariadi^{1

3}, Han-Cheng Wang^{4

5

6}, Chiehfeng Chen^{7

8

9}

Affiliations

¹ Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan.
² Department of Information System, Universitas Brawijaya, Malang, Indonesia.
³ Department of Industrial Engineering and Management, Bandung Institute of Technology, Bandung, Indonesia.
⁴ Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan.
⁵ College of Medicine, National Taiwan University, Taipei, Taiwan.
⁶ College of Medicine, Taipei Medical University, Taipei, Taiwan.
⁷ Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
⁸ Division of Plastic Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
⁹ Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.

Abstract

Background and objective: Increases in outpatients seeking medical check-ups are expanding the number of health examination data records, which can be utilized for medical strategic planning and other purposes. However, because hospital visits by outpatients seeking medical check-ups are unpredictable, those patients often cannot receive optimal service due to limited facilities of hospitals. To resolve this problem, this study attempted to predict re-visit patterns of outpatients.

Method: Two-phase sequential pattern mining (SPM) and an association mining method were chosen to predict patient returns using sequential data. The data were grouped according to the outpatients' personal information and evaluated by a discriminant analysis to check the significance of the grouping. Furthermore, SPM was employed to generate frequency patterns from each group and extract a general association pattern of return.

Results: Results of sequence patterns and association mining in this study provided valuable insights in terms of outpatients' re-visit behaviors for regular medical check-ups. Cosine and Jaccard are two symmetric measures which were used in this study to indicate the degree of association between two variables. For instance, Jaccard values of variable abnormal blood pressure associated with an abnormal body-mass index (BMI) and/or abnormal blood sugar were respectively 47.5% and 100%, for the two-visit and three-visit behavior patterns. These results indicated that the corresponding pair of variables was more reliable when covering the three-visit behavior pattern than the two-visit behavior. Thus, appropriate preventive measures or suggestions for other medical treatments can be prepared for outpatients that have this pattern on their third visit. The higher degree of association implies that the corresponding behavior pattern might influence outpatients' intentions to regularly seek medical check-ups concerning the risk of stroke. Furthermore, a radiology diagnosis (i.e., magnetic resonance imaging or neck vascular ultrasound) plays an important role in the association with a re-visit behavior pattern with respective 50% and 70% Cosine and Jaccard values in general behavior {f11}∧{f01}. These findings can serve as valuable information to increase the quality of medical services and marketing, by suggesting appropriate treatment for the subsequent visit after learning the behavior patterns.

Conclusions: The proposed method can provide valuable information related to outpatients' re-visit behavior patterns based on hidden knowledge generated from sequential patterns and association mining results. For marketing purposes, medical practitioners can take behavior patterns studied in this paper into account to raise patients' awareness of several possible medical conditions that might arise on subsequent visits and encourage them to take preventive measures or suggest other medical treatments.

Keywords: Cerebrovascular disease; Re-visit behavior patterns.; Sequential Pattern Mining.

Grants and funding

The authors received support from Taipei Medical University and the National Taiwan University of Science and Technology through project no. TMU-NTUST-106-08. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.