- Develop a platform for pattern detection and insights on patient data.
- Identify sequences leading to life-threatening hypo/hyperglycemic events.
- Create dynamic features based on user requirements.
- Filter patients based on specified conditions.
- Display outcomes for events across variable timeframes.
- Train models on multiple target columns for single/multiple patients.
- Gained insights into patient behavior through frequent pattern observation.
- Utilized machine learning algorithms (Apriori, Random Forest, XGBoost) for advanced analysis.
- Provisioned a highly scalable cloud-based user interface for dynamic feature generation.
- Generates alerts by predicting patient behavior.
- Provided actionable insights for corrective measures and lifestyle recommendations.
Improvement in patient outcomes
Boost in CSAT – patient delight for receiving recommendations in their inbox
Clinical support team able to resolve patient calls in half the time.