Alert system branch tran

[AnNguyen87]

Description

Implemented a comprehensive anomaly detection system for the Guardian Monitor alert system. This PR adds LSTM-based pattern recognition for patient vitals and behavioral data, with patient-specific threshold tuning and a real-time alert engine.

Key Features Added:
Developed LSTM neural network for time-series analysis of patient vitals and ADLs
Implemented patient-specific anomaly detection thresholds using percentile-based approach
Built real-time alerting engine with severity-based notifications for nursing staff and guardians
Added validation metrics system to track false positive/negative rates and timeliness

Technical Implementation:
Used TensorFlow/Keras for model development
Created data preprocessing pipeline for vitals and behavioral data
Implemented adaptive thresholding per patient based on historical patterns
Designed modular alert system with configurable notification rules

Todos

• Tested and working locally with synthetic patient data
• Code follows Python style guidelines (PEP 8)
• I have performed a self-review of my code
• Code changes documented
• Requested review from >= 2 devs on the team (one frontend and one backend recommended)

How to test

Data Preparation:
python DataGenerator.ipynb

Model Training:
python model_training.py

Test Anomaly Detection:
python test_anomaly_detection.py

Test Alert System:
python test_alert_system.py

View Metrics:
python generate_validation_report.py

Associated MS Planner Tasks

• Guardian Alert System with Emotional & Health Risk Detection - Model Development (https://teams.microsoft.com/l/entity/com.microsoft.teamspace.tab.planner/planner.v1.1c7aff73-bd5d-4eaf-844f-a0f0914d35b0_p_obnY9eRGNEGXqjBUOhDWKcgAFWt1?tenantId=d02378ec-1688-46d5-8540-1c28b5f470f6&webUrl=https%3A%2F%2Ftasks.teams.microsoft.com%2Fteamsui%2FpersonalApp%2Falltasklists&context=%7B%22subEntityId%22%3A%22%2Fv1%2Fplan%2FobnY9eRGNEGXqjBUOhDWKcgAFWt1%2Fview%2Fboard%2Ftask%2F7lBlc9whxU-Qy1L3kVSoFcgAPTEw%22%2C%22channelId%22%3A%2219%3A02e7479536e247c5a85093cf6dcaca4c%40thread.tacv2%22%7D)

Known Issues

LSTM model may be computationally intensive for very long sequences, Requires minimum of 6 time points per patient for accurate predictions.