AI Health Analyzer

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

• ✅ "AI analyze my health status"
• ✅ "What health risks do I have?"
• ✅ "Generate AI health report"
• ✅ "AI analyze all data"

• ✅ "Predict my hypertension risk"
• ✅ "Am I at risk of diabetes?"
• ✅ "Assess my cardiovascular risk"
• ✅ "AI predict health risks"

• ✅ "How is my sleep?"
• ✅ "What impact does exercise have on my health?"
• ✅ "How should I improve my health?"
• ✅ "AI health assistant Q&A"

• ✅ "Generate AI health report"
• ✅ "Create comprehensive analysis report"
• ✅ "AI risk assessment report"

Execution Steps

Step 1: Read AI Configuration

const aiConfig = readFile('data/ai-config.json');
const aiHistory = readFile('data/ai-history.json');

Step 2: Read User Profile

const profile = readFile('data/profile.json');

Step 3: Read Health Data

// 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

Step 5: Multi-dimensional Analysis

Step 6: Risk Prediction

• 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

• 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

scripts/generate_ai_report.py

Step 9: Update AI History

data/ai-history.json

Data Sources

data/profile.json

data/index.json

data-example/fitness-tracker.json

data-example/sleep-tracker.json

data-example/nutrition-tracker.json

data-example/mental-health-tracker.json

data/medications.json

data/allergies.json

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 analyze

  - AI comprehensive analysis

• /ai predict [risk_type]

  - Health risk prediction

• /ai chat [query]

  - Natural language Q&A

• /ai report generate [type]

  - Generate AI health report

• /ai status

  - Check AI function status

Technical Implementation

Tool Restrictions

• 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