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Leveraging Unsupervised Machine Learning to Discover Patterns in Linguistic Health Summaries for Eldercare
Summary
Smart sensors in aging-in-place communities detect health anomalies in older adults. Analyzing these patterns helps identify health conditions for predictive and preventative care.
Area of Science:
- Gerontology and Health Informatics
- Computational Health Science
- Data Mining in Healthcare
Background:
- The Center for Eldercare and Rehabilitation Technology has extensively researched smart sensor technology for monitoring older adults in aging-in-place communities.
- Daily sensor data from residents' apartments are aggregated and analyzed to support health awareness, clinical care, and healthy aging research.
- Fuzzy computational techniques convert sensor data anomalies into linguistic health messages for clinicians.
Purpose of the Study:
- To discover combinations of co-occurring and recurrent anomaly patterns within the older adult population using text summaries of sensor data.
- To extract analytical features from health messages using computational text data processing techniques.
- To apply frequent pattern mining for association rule discovery to identify population-level health patterns.
Main Methods:
- Sensor data were processed to extract analytical features from linguistic health messages.
- Features were transformed into a transactional encoding for frequent pattern mining.
- Association rule discovery was employed to identify combinations of anomalies and their recurrence.
Main Results:
- At the individual level, seven combinations of anomalies were discovered for resident ID 3027, with one group showing increased recurrence during COVID lockdown.
- At the population level, 38 associations highlighting health patterns were discovered.
- Exploration of health conditions associated with these patterns is ongoing.
Conclusions:
- The study successfully identified combinations of anomalies and recurrent patterns in older adult health data.
- These discovered associations can be correlated with specific health conditions for predictive analytics.
- The findings aim to enhance clinical care systems for older adults in smart sensor-equipped aging-in-place communities through preventative strategies.
