AI Health Analyzer
An AI-powered comprehensive health analysis system that provides intelligent health insights, risk prediction, and personalized recommendations.
Core Features
1. Intelligent Health Analysis
- Multi-dimensional data integration: Integrates 4 types of data sources including basic indicators, lifestyle, mental health, and medical history
- Abnormal pattern recognition: Detects outliers and change points using algorithms like CUSUM and Z-score
- Correlation analysis: Calculates correlations between different health indicators (Pearson, Spearman)
- Trend prediction: Conducts trend analysis and prediction based on historical data
2. Health Risk Prediction
- Hypertension risk: Based on the Framingham Risk Score model
- Diabetes risk: Based on ADA Diabetes Risk Criteria
- Cardiovascular disease risk: Based on ACC/AHA ASCVD guidelines
- Nutritional deficiency risk: Based on RDA achievement rate and dietary pattern analysis
- Sleep disorder risk: Based on PSQI and sleep pattern analysis
3. Personalized Recommendation Engine
- Basic personalization: Based on static profiles such as age, gender, BMI, and activity level
- Recommendation grading: Level 1 (general), Level 2 (reference), Level 3 (medical advice)
- Evidence-based: Based on medical guidelines and evidence-based medicine
- Operability: Provides specific, feasible improvement suggestions
4. Natural Language Interaction
- Intelligent Q&A: Supports health data queries, trend analysis, correlation queries, etc.
- Context understanding: Maintains conversation history and supports multi-turn dialogue
- Intent recognition: Identifies user query intent and provides precise responses
5. AI Health Report Generation
- Comprehensive report: Includes multi-dimensional health data, AI insights, and risk assessment
- Quick summary: Overview of key indicators, anomaly alerts, and main recommendations
- Risk assessment report: Various disease risks, risk factor analysis, and preventive measures
- Trend analysis report: Multi-dimensional trends, change point identification, and predictive analysis
- Interactive HTML report: ECharts charts, Tailwind CSS styling
Usage Instructions
Trigger Conditions
Use this skill when the user mentions the following scenarios:
General Inquiries:
- ✅ "AI analyze my health status"
- ✅ "What health risks do I have?"
- ✅ "Generate AI health report"
- ✅ "AI analyze all data"
Risk Prediction:
- ✅ "Predict my hypertension risk"
- ✅ "Am I at risk of diabetes?"
- ✅ "Assess my cardiovascular risk"
- ✅ "AI predict health risks"
Intelligent Q&A:
- ✅ "How is my sleep?"
- ✅ "What impact does exercise have on my health?"
- ✅ "How should I improve my health?"
- ✅ "AI health assistant Q&A"
Report Generation:
- ✅ "Generate AI health report"
- ✅ "Create comprehensive analysis report"
- ✅ "AI risk assessment report"
Execution Steps
Step 1: Read AI Configuration
javascript
const aiConfig = readFile('data/ai-config.json');
const aiHistory = readFile('data/ai-history.json');
Check if AI functions are enabled and verify data source configurations.
Step 2: Read User Profile
javascript
const profile = readFile('data/profile.json');
Obtain basic information: age, gender, height, weight, BMI, etc.
Step 3: Read Health Data
Read relevant data according to configured data sources:
javascript
// Basic health indicators
const indexData = readFile('data/index.json');
// Lifestyle data
const fitnessData = readFile('data-example/fitness-tracker.json');
const sleepData = readFile('data-example/sleep-tracker.json');
const nutritionData = readFile('data-example/nutrition-tracker.json');
// Mental health data
const mentalHealthData = readFile('data-example/mental-health-tracker.json');
// Medical history
const medications = exists('data/medications.json') ? readFile('data/medications.json') : null;
const allergies = exists('data/allergies.json') ? readFile('data/allergies.json') : null;
Step 4: Data Integration and Preprocessing
Integrate all data sources, perform data cleaning, time alignment, and missing value handling.
Step 5: Multi-dimensional Analysis
Correlation analysis: Calculate correlations such as sleep ↔ mood, exercise ↔ weight, nutrition ↔ biochemical indicators
Trend analysis: Use methods like linear regression and moving average to identify trend directions
Anomaly detection: Detect outliers and change points using CUSUM and Z-score algorithms
Step 6: Risk Prediction
Perform risk prediction based on standards such as Framingham, ADA, and ACC/AHA:
- Hypertension risk (10-year probability)
- Diabetes risk (10-year probability)
- Cardiovascular disease risk (10-year probability)
- Nutritional deficiency risk
- Sleep disorder risk
Step 7: Generate Personalized Recommendations
Generate three levels of recommendations based on analysis results:
- Level 1: General recommendations (based on standard guidelines)
- Level 2: Reference recommendations (based on personal data)
- Level 3: Medical advice (requires doctor confirmation, includes disclaimer)
Step 8: Generate Analysis Report
Text report: Includes overall assessment, risk prediction, key trends, correlation findings, personalized recommendations
HTML report: Call
scripts/generate_ai_report.py
to generate interactive report with ECharts charts
Step 9: Update AI History
Record analysis results to
Data Sources
| Data Source | File Path | Data Content |
|---|
| User Profile | | Age, gender, height, weight, BMI |
| Medical Records | | Biochemical indicators, imaging examinations |
| Fitness Tracker | data-example/fitness-tracker.json
| Exercise type, duration, intensity, MET value |
| Sleep Tracker | data-example/sleep-tracker.json
| Sleep duration, quality, PSQI score |
| Nutrition Tracker | data-example/nutrition-tracker.json
| Diet records, nutrient intake, RDA achievement rate |
| Mental Health | data-example/mental-health-tracker.json
| PHQ-9, GAD-7 scores |
| Medication Records | | Drug name, dosage, usage, compliance |
| Allergy History | | Allergens, severity |
Algorithm Explanation
Correlation Analysis
- Pearson correlation coefficient: For continuous variables (e.g., sleep duration vs. mood score)
- Spearman correlation coefficient: For ordinal variables (e.g., symptom severity)
Anomaly Detection
- CUSUM algorithm: Time series change point detection
- Z-score method: Statistical outlier detection (|z| > 2)
- IQR method: Interquartile range outlier detection
Risk Prediction
- Framingham Risk Score: Hypertension, cardiovascular disease risk
- ADA Risk Score: Type 2 diabetes risk
- ASCVD Calculator: Atherosclerotic cardiovascular disease risk
Safety and Compliance
Must Follow
- ❌ Do not provide medical diagnosis
- ❌ Do not provide specific medication dosage recommendations
- ❌ Do not judge life-or-death prognosis
- ❌ Do not replace doctor's advice
- ✅ All analysis must be labeled "For reference only"
- ✅ Level 3 recommendations must include a disclaimer
- ✅ High-risk predictions must advise consulting a doctor
Privacy Protection
- ✅ All data remains local
- ✅ No external API calls
- ✅ HTML report runs independently
Related Commands
- - AI comprehensive analysis
- - Health risk prediction
- - Natural language Q&A
/ai report generate [type]
- - Check AI function status
Technical Implementation
Tool Restrictions
This Skill only uses the following tools:
- Read: Read JSON data files
- Grep: Search for specific patterns
- Glob: Find data files by pattern
- Write: Generate HTML reports and update history records
Performance Optimization
- Incremental reading: Only read data files within the specified time range
- Data caching: Avoid repeated reading of the same file
- Lazy calculation: Generate chart data on demand