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Skillsai-analyzer

ai-analyzer

Data & analytics

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Updated 10-15 22:15

About this skill

AI Health Analyzer

A comprehensive health analysis system based on AI technology, providing 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: Uses algorithms such as CUSUM and Z-score to detect outliers and change points
  • Correlation Analysis: Calculates correlations between different health indicators (Pearson, Spearman)
  • Trend Prediction: Performs 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 scoring standards
  • Cardiovascular Disease Risk: Based on ACC/AHA ASCVD guidelines
  • Nutritional Deficiency Risk: Based on RDA achievement rates 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 Basis: Based on medical guidelines and evidence-based medicine
  • Actionability: Provides specific and 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: Recognizes user query intent and provides precise responses

5. AI Health Report Generation

  • Comprehensive Report: Includes all dimensions of health data, AI insights, and risk assessment
  • Quick Summary: Overview of key indicators, abnormal 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
  • HTML Interactive 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 an AI health report"
  • ✅ "AI analyze all data"

Risk Prediction:

  • ✅ "Predict my hypertension risk"
  • ✅ "Do I have diabetes risk?"
  • ✅ "Assess my cardiovascular risk"
  • ✅ "AI predict health risks"

Intelligent Q&A:

  • ✅ "How is my sleep?"
  • ✅ "How does exercise affect 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

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

Check whether the AI feature is enabled and verify the data source configuration.

Step 2: Read User Profile

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 the configured data sources:

// 基础健康指标
const indexData = readFile('data/index.json');

// 生活方式数据
const fitnessData = readFile('data-example/fitness-tracker.json');
const sleepData = readFile('data-example/sleep-tracker.json');
const nutritionData = readFile('data-example/nutrition-tracker.json');

// 心理健康数据
const mentalHealthData = readFile('data-example/mental-health-tracker.json');

// 医疗历史
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 and perform data cleaning, time alignment, and missing value handling.

Step 5: Multidimensional Analysis

Correlation Analysis: Calculate associations such as sleep ↔ mood, exercise ↔ weight, nutrition ↔ biochemical indicators, etc.

Trend Analysis: Use linear regression, moving average, and other methods to identify trend direction

Anomaly Detection: Use CUSUM, Z-score algorithms to detect outliers and change points

Step 6: Risk Prediction

Perform risk prediction based on standards such as Framingham, ADA, ACC/AHA, etc.:

  • 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 the analysis results:

  • Level 1: General recommendations (based on standard guidelines)
  • Level 2: Reference recommendations (based on personal data)
  • Level 3: Medical recommendations (require physician confirmation, include 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 an interactive report containing ECharts charts

Step 9: Update AI History

Record the analysis results to data/ai-history.json

Data Sources

Data Source File Path Data Content
User Profile data/profile.json Age, gender, height, weight, BMI
Medical Records data/index.json Biochemical indicators, imaging examinations
Exercise Tracking data-example/fitness-tracker.json Exercise type, duration, intensity, MET value
Sleep Tracking data-example/sleep-tracker.json Sleep duration, quality, PSQI score
Nutrition Tracking 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 data/medications.json Medication name, dosage, usage, adherence
Allergy History data/allergies.json Allergens, severity

Algorithm Description

Correlation Analysis

  • Pearson correlation coefficient: Continuous variables (such as sleep duration and mood scores)
  • Spearman correlation coefficient: Ordinal variables (such as symptom severity)

Anomaly Detection

  • CUSUM Algorithm: Time series change point detection
  • Z-score Method: Statistical outlier detection (|z| > 2)
  • IQR Method: Quartile-based 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 diagnoses
  • ❌ Do not provide specific medication dosage recommendations
  • ❌ Do not determine life or death prognosis
  • ❌ Do not replace doctor's advice
  • ✅ All analyses must be labeled "for reference only"
  • ✅ Level 3 recommendations must include a disclaimer
  • ✅ High-risk predictions must recommend consulting a doctor

Privacy Protection

  • ✅ All data remains local
  • ✅ No external API calls
  • ✅ HTML reports run 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 - View AI feature status

Technical Implementation

Tool Limitations

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 repeatedly reading the same file
  • Deferred computation: generate chart data on demand