Health Trend Analyzer

Core Features

1. Multi-dimensional Trend Analysis

• Weight/BMI Trends: Track changes in weight and BMI over time to evaluate health trends
• Symptom Patterns: Identify recurring symptoms, frequency changes, and potential triggers
• Medication Adherence: Analyze medication-taking patterns, identify missed dose patterns, and find areas for improvement
• Lab Result Trends: Track changes in biochemical indicators (cholesterol, blood glucose, blood pressure, etc.)
• Mood and Sleep: Correlate emotional states with sleep quality to identify mental health trends

2. Correlation Analysis Engine

• Medication-Symptom Correlation: Identify if new medications are associated with symptom changes
• Lifestyle Impact: Correlate diet/sleep with symptoms and mood
• Treatment Effect Evaluation: Measure if treatments lead to improvements
• Cycle-Symptom Correlation: Cycle correlation in women's health tracking

3. Change Detection

• Significant Changes: Warn of rapid weight changes, new symptoms, and medication changes
• Deterioration Patterns: Early identification of declining health status
• Improvement Recognition: Highlight positive health changes
• Threshold Alerts: Warn when approaching dangerous levels (radiation, BMI extremes)

4. Predictive Insights

• Risk Assessment: Identify risk factors based on trends
• Prevention Recommendations: Suggest preventive measures based on patterns
• Early Warning: Predict issues before they become serious

Usage Instructions

Trigger Conditions

• ✅ "What changes have occurred in my health over the past period?"
• ✅ "Analyze my health trends"
• ✅ "What's changed with my physical condition?"
• ✅ "Health status summary"

• ✅ "What's the trend of my weight/BMI?"
• ✅ "Analyze my symptom patterns"
• ✅ "How is my medication adherence?"
• ✅ "What changes are there in my lab indicators?"
• ✅ "My mood and sleep trends"

• ✅ "What are my symptoms related to?"
• ✅ "Is my medication effective?"
• ✅ "What's the relationship between sleep and my mood?"

• Default analysis of past 3 months of data
• Supported: "Past 1 month", "Past 6 months", "Past 1 year"
• Supported: "From January 2025 to present", "Last 90 days"

Execution Steps

Step 1: Determine Analysis Time Range

Step 2: Read Health Data

// 1. 个人档案（BMI、体重）
const profile = readFile('data/profile.json');

// 2. 症状记录
const symptomFiles = glob('data/symptoms/**/*.json');
const symptoms = readAllJson(symptomFiles);

// 3. 情绪记录
const moodFiles = glob('data/mood/**/*.json');
const moods = readAllJson(moodFiles);

// 4. 饮食记录
const dietFiles = glob('data/diet/**/*.json');
const diets = readAllJson(dietFiles);

// 5. 用药日志
const medicationLogs = glob('data/medication-logs/**/*.json');

// 6. 女性健康数据（如适用）
const cycleData = readFile('data/cycle-tracker.json');
const pregnancyData = readFile('data/pregnancy-tracker.json');
const menopauseData = readFile('data/menopause-tracker.json');

// 7. 过敏史
const allergies = readFile('data/allergies.json');

// 8. 辐射记录
const radiation = readFile('data/radiation-records.json');

Step 3: Data Filtering

function filterByDate(data, startDate, endDate) {
  return data.filter(item => {
    const itemDate = new Date(item.date || item.created_at);
    return itemDate >= startDate && itemDate <= endDate;
  });
}

Step 4: Trend Analysis

• Extract historical weight data
• Calculate BMI changes
• Identify trend direction (upward/downward/stable)
• Evaluate change magnitude

• Count symptom frequencies
• Identify high-frequency symptoms
• Analyze symptom time patterns
• Detect symptom triggers

• Calculate overall adherence rate
• Analyze adherence for each medication
• Identify missed dose patterns
• Evaluate improvement suggestions

• Track biochemical indicators across multiple reports
• Compare with reference ranges
• Identify improvements/deterioration
• Mark abnormal indicators

• Correlate mood scores with sleep duration
• Identify mood fluctuation patterns
• Detect stress levels
• Evaluate mental health trends

Step 5: Correlation Analysis

// 皮尔逊相关系数
function pearsonCorrelation(x, y) {
  // 计算相关系数
  // 返回值范围：-1（负相关）到 1（正相关）
}

// 应用场景
- 药物开始日期 vs 症状频率
- 睡眠时长 vs 情绪评分
- 体重变化 vs 饮食记录
- 运动量 vs 情绪状态

Step 6: Change Detection

// 变化点检测
function detectChangePoints(timeSeries) {
  // 使用统计方法检测显著变化点
  // 例如：体重突然下降、症状突然增加
}

// 阈值警报
function checkThresholds(value, thresholds) {
  // 检查是否接近或超过危险阈值
  // 例如：BMI > 30、辐射剂量 > 安全限
}

Step 7: Generate Insights

// 风险评估
function assessRisks(trends) {
  // 识别高风险趋势
  // 例如：快速体重下降、频繁症状
}

// 预防建议
function generateRecommendations(trends, correlations) {
  // 基于模式建议预防措施
  // 例如：改善睡眠、提高用药依从性
}

// 早期预警
function earlyWarnings(trends) {
  // 在问题变得严重之前预测
  // 例如：症状频率上升、情绪持续低落
}

Step 8: Generate Visualization Report

1. Data Summary: Generate analysis results in JSON format
2. HTML Template Rendering: Inject data into the HTML template
3. ECharts Chart Configuration: Configure 6 types of interactive charts
4. Save File: Save as a standalone HTML file

Output Formats

Text Report (Concise Version)

Health Trend Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━
Generation Time: 2025-12-31
Analysis Period: Past 3 months (2025-10-01 to 2025-12-31)

📊 Overall Assessment
━━━━━━━━━━━━━━━━━━━━━━━━━━
Improving: Weight management, cholesterol levels
Stable: Blood glucose control, emotional state
Needs Attention: Medication adherence, sleep quality

📊 Weight/BMI Trends
├─ Current Weight: 68.5 kg
├─ Current BMI: 23.1 (Normal Range)
├─ 3-month Change: -2.3 kg (-3.2%)
├─ Trend: 📉 Gradual weight loss
└─ Assessment: ✅ Positive trend, within healthy range

💊 Medication Adherence
├─ Current Medications: 3 types
├─ Overall Adherence Rate: 78%
├─ Missed Doses: 8 times
├─ Best: Aspirin (95%)
└─ Needs Improvement: Amlodipine (65%)

⚠️ Symptom Patterns
├─ Most Frequent: Headache (12 times in past 3 months)
├─ Trend: 📉 Decreasing frequency (4 fewer than previous period)
├─ Potential Trigger: Moderate correlation identified with sleep quality (r=0.62)
└─ Recommendation: Continue improving sleep patterns

🧪 Lab Result Trends
├─ Cholesterol: 240 → 210 mg/dL (Improved ✅)
├─ Blood Glucose: 5.6 → 5.4 mmol/L (Stable)
├─ Last Check: 30 days ago
└─ Recommendation: Recheck in 3 months

😊 Mood and Sleep
├─ Average Mood Score: 6.8/10
├─ Average Sleep Duration: 6.5 hours
├─ Trend: Stable mood, slight improvement in sleep
└─ Correlation: Strong correlation between sleep duration and mood score (r=0.78)

🔗 Correlation Analysis
━━━━━━━━━━━━━━━━━━━━━━━━━━
• Sleep Duration ↔ Mood Score: Strong positive correlation (r=0.78)
• Weight Change ↔ Diet Records: Moderate correlation (r=0.55)
• Medication Adherence ↔ Symptom Frequency: Moderate negative correlation (r=-0.62)

💡 Risk Assessment and Recommendations
━━━━━━━━━━━━━━━━━━━━━━━━━━

🟢 Keep Up the Good Work
• Current weight management methods are effective
• Cholesterol levels have improved significantly

🟡 Needs Attention
• Improve amlodipine adherence (set reminders)
• Increase sleep duration to 7-8 hours

📅 Follow-up Plan
• Recheck lipid panel in 3 months
• Evaluate medication adherence improvement in 1 month

━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ Disclaimer
This analysis is for reference only and does not replace professional medical diagnosis.
Please consult a doctor for professional advice.

HTML Visualization Report (Full Version)

1. Overall Assessment Card: Key indicators at a glance
2. Weight/BMI Trend Chart: Dual Y-axis line chart (weight + BMI)
3. Symptom Frequency Chart: Color-coded bar chart (high frequency red/medium frequency yellow/low frequency green)
4. Medication Adherence Dashboard: Overall adherence rate + details for each medication
5. Lab Result Trend Chart: Multi-series line chart + reference lines
6. Correlation Heatmap: Heatmap showing correlations between variables
7. Mood and Sleep Area Chart: Dual Y-axis area chart

• ✅ Fully standalone (all dependencies via CDN)
• ✅ Interactive charts (zoom, export, legend toggle)
• ✅ Responsive design (mobile-adapted)
• ✅ Printable (print-optimized styles)
• ✅ Shareable (send to doctors)

Data Sources

Primary Data Sources

data/profile.json

data/symptoms/**/*.json

data/mood/**/*.json

data/diet/**/*.json

data/medication-logs/**/*.json

data/medical_records/**/*.json

Secondary Data Sources

data/cycle-tracker.json

data/pregnancy-tracker.json

data/menopause-tracker.json

data/allergies.json

data/radiation-records.json

Analysis Algorithms

Time Series Analysis

• Trend detection (linear regression)
• Seasonal analysis
• Outlier detection

Correlation Analysis

• Pearson correlation coefficient (continuous variables)
• Spearman correlation coefficient (ordinal variables)
• Cross-correlation analysis (time series)

Change Point Detection

• CUSUM algorithm
• Sliding window t-test
• Bayesian change point detection

Statistical Indicators

• Mean, median, standard deviation
• Percentiles (25%, 50%, 75%)
• Change rates (month-over-month, year-over-year)

Safety and Privacy

Must Follow

• ❌ Do not provide medical diagnoses
• ❌ Do not provide specific medication advice
• ❌ Do not judge life-or-death prognosis
• ❌ Include a disclaimer (for reference only)

Information Accuracy

• ✅ Only analyze based on recorded data
• ✅ Do not speculate or infer missing information
• ✅ Clearly label data sources and time ranges
• ✅ Recommendations should be reviewed by medical professionals

Privacy Protection

• ✅ All data remains local
• ✅ No external API calls
• ✅ Analysis results are only stored locally
• ✅ HTML reports run independently (no data transmission)

Error Handling

Data Missing

• No Data: Output "No data available. It is recommended to record [data type] first"
• Insufficient Data: Output "Insufficient data (at least 1 month of data is required for trend analysis)"
• Narrow Data Range: Use existing data and prompt "It is recommended to extend the recording time for more accurate trends"

Analysis Failure

• Unable to Calculate Trends: Output "Unable to calculate trends due to insufficient data points"
• Correlation Analysis Failure: Output "More data is needed for correlation analysis"
• Chart Rendering Failure: Degrade to text report

Usage Examples

Example 1: General Health Trends

Example 2: Symptom Analysis

Example 3: Weight Trends

Example 4: Medication Effectiveness

Related Commands

• /symptom

  : Record symptoms

• /mood

  : Record mood

• /diet

  : Record diet

• /medication

  : Manage medications and medication records

• /query

  : Query specific data points

Technical Implementation

Tool Limitations

• Read: Read JSON data files
• Grep: Search for specific patterns
• Glob: Find data files by pattern
• Write: Generate HTML reports (save to

  data/health-reports/

  )

Performance Optimization

• Incremental reading: Only read data files within the specified time range
• Data caching: Avoid re-reading the same file
• Lazy calculation: Generate chart data on demand

Extensibility

• Support adding new data dimensions
• Support custom chart types
• Support custom analysis algorithms