🧠 Sensor Intelligence Workspace

Building AI/ML systems that make glucose data better than Clarity's cleaning

🎯 Mission: Transform the -2.2 mmol/L overlap bias discovery and sensor lifetime patterns into practical AI algorithms that predict sensor quality, optimize calibrations, and create personalized glucose accuracy better than raw Clarity data.
6
Phase 1 Tasks Done
5
Critical Gaps Found
13
G7 Overlap Periods
557K
Records Consolidated

📊 Phase 1: Data Foundation & Discovery

CRITICAL GAPS FOUND
⚠️ Foundation Issues Discovered: Data processing has critical gaps that must be fixed before AI/ML analysis.
  • ✓
    Establish rock-solid file structure for medical data HIGH
  • ✓
    Process 5-year Clarity data with Event Type filtering HIGH
  • ✓
    Discover G6→G7 transition patterns in data HIGH
  • ✓
    Find 13 G7 overlap periods from magnetic auto-start HIGH
  • ✓
    Analyze -2.2 mmol/L average bias in new sensors HIGH
  • ✓
    Create single consolidated 5-year file HIGH
  • ☐
    🩸 Teach Claude logic behind Dexcom calibration entries CRITICAL
  • ☐
    ⏰ Add rounded datetime column (5-min intervals: 6:00, 6:05, 6:10) CRITICAL
  • ☐
    🔍 Check what happens to Blood Glucose entries in processing CRITICAL
  • ☐
    🔄 Revisit overlaps in detail with proper calibration logic CRITICAL
  • ☐
    📱 Investigate backfill logic for missed readings when away from phone CRITICAL
🔍 Key Discoveries:
346
Time-matched pairs
98.8%
New sensors read lower
709
Calibrations (first 3 days)
1M+
Clean glucose readings
📊 View 24 Overlap Charts

🤖 Phase 2: AI Algorithm Development

IN PROGRESS
  • Build sensor quality prediction model (6-12 hour window) HIGH
  • Create bias correction algorithm using -2.2 mmol/L pattern HIGH
  • Develop calibration optimization predictor HIGH
  • Extract volatility and noise patterns by sensor day MEDIUM
  • Implement early termination detection (day 1-3) MEDIUM
  • Build G6 vs G7 accuracy comparison model MEDIUM
🎯 Next Action:
Start with bias correction algorithm - we have solid -2.2 mmol/L baseline and 346 time-matched pairs for training

👤 Phase 3: Christina-Specific Intelligence

PLANNED
  • Create sensor insertion tracking system HIGH
  • Build location-specific performance database HIGH
  • Track pinching technique effectiveness MEDIUM
  • Monitor time-of-day insertion success patterns MEDIUM
  • Correlate exercise/meal timing with accuracy LOW
  • Develop personal sensor rotation guidance MEDIUM
🎯 Personalization Goals:
  • Reduce 20% early termination rate to <10%
  • Minimize calibration burden in first 3 days
  • Predict sensor success within 12 hours
  • Build Christina-specific accuracy models

⚡ Phase 4: Real-time Intelligence System

FUTURE
  • Build real-time sensor quality monitoring HIGH
  • Create smart alert system for sensor issues HIGH
  • Implement confidence intervals for readings MEDIUM
  • Develop predictive accuracy degradation alerts MEDIUM
  • Build optimal replacement timing system MEDIUM
  • Create cloud-based pattern learning system LOW
🔮 Vision:
Real-time system that continuously improves glucose accuracy using AI, making Christina's data better than any commercial solution

📅 Development Timeline

July 19, 2025
Foundation Complete
Discovered -2.2 mmol/L bias pattern and 24 overlap periods. Ready for AI development.
Next: July 20-25
Bias Correction Algorithm
Build and test model to correct new sensor readings using overlap data patterns.
Next: July 26-31
Sensor Quality Prediction
Develop early warning system for sensors that won't become accurate by day 3.
Future: August
Personalization System
Implement insertion tracking and Christina-specific optimization.