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Profiling of patients with type 2 diabetes based on medication adherence data
Rene Markovič1,2, Vladimir Grubelnik2, Tadej Završnik3,4
1Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia.
Frontiers in Public Health
|July 24, 2023
Summary
Patient profiling for type 2 diabetes mellitus (T2DM) reveals distinct medication use patterns across age groups. Early and late age groups show simple profiles, while middle-aged individuals exhibit complex patterns linked to T2DM complications.
Area of Science:
- Endocrinology and Diabetology
- Medical Informatics
- Data Mining
Background:
- Type 2 diabetes mellitus (T2DM) is a complex, chronic condition impacting multiple organs.
- Patient profiling is crucial for identifying individuals with unfavorable disease progression and tailoring therapies.
- Understanding disease phases and medication use is key to managing T2DM.
Purpose of the Study:
- To uncover distinct T2DM patient profiles using medication intake and laboratory data.
- To analyze how individuals with diabetes progress through different disease phases.
- To identify patient subgroups for personalized treatment strategies.
Main Methods:
- Utilized 20-year medical records from a university medical center.
- Created patient-specific drug profiles using ATC classification.
- Applied natural language processing, data mining, and hierarchical clustering.
Main Results:
- Identified well-structured profile distributions across age groups.
- Early (40-50) and late (80+) age groups showed only two distinct medication use profiles.
- Middle-aged groups (55-75) exhibited several complex profiles associated with T2DM complications.
Conclusions:
- The developed profiling approach is widely implementable and cost-effective.
- Readily available treatment and drug use data facilitate global profiling insights.
- Integrating data from other specialties can enhance T2DM patient profile sophistication.
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