Related Experiment Videos
Applying sequential pattern mining to investigate cerebrovascular health outpatients' re-visit patterns
Chao Ou-Yang1, Chandrawati Putri Wulandari1,2, Rizka Aisha Rahmi Hariadi1,3
1Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan.
Peerj
|July 18, 2018
Summary
Predicting outpatient re-visit patterns using sequential and association mining helps optimize hospital services. Understanding patient behavior aids in proactive healthcare and improved medical strategic planning.
Area of Science:
- Health Informatics
- Data Mining
- Predictive Analytics
Background:
- Increasing outpatient visits generate vast health data.
- Unpredictable patient flow strains hospital resources and service quality.
- Predicting re-visit patterns is crucial for efficient healthcare management.
Purpose of the Study:
- To predict outpatient re-visit patterns using data mining techniques.
- To enhance medical strategic planning and optimize hospital services.
- To identify key factors influencing patient return behavior.
Main Methods:
- Two-phase sequential pattern mining (SPM) and association mining.
- Grouping patient data by personal information and discriminant analysis.
- Extracting general association patterns of patient re-visits.
Main Results:
- Identified significant associations between health indicators (e.g., abnormal BMI, blood pressure) and re-visit patterns.
- Demonstrated higher reliability of patterns with three visits compared to two.
- Radiology diagnoses like MRI and neck ultrasound showed strong associations with re-visit behavior.
Conclusions:
- The proposed method effectively reveals outpatient re-visit behavior patterns.
- Findings support proactive healthcare interventions and personalized treatment suggestions.
- Insights can improve medical service quality and marketing strategies.