chore: automated code quality fixes (npx)

.local/state/replit/agent/.latest.json

@@ -1 +1 @@
-{"latest": "main"}
+{ "latest": "main" }

ANALYSIS_README.md

@@ -14,6 +14,7 @@ This document describes the Python-based data analysis and clinical decision sup
 ### 1. Load and Explore ✅
 
 The script loads the diabetes dataset and provides comprehensive exploratory analysis:
+
 - Dataset shape, columns, data types
 - Summary statistics for all numeric features
 - Missing value detection
@@ -22,6 +23,7 @@ The script loads the diabetes dataset and provides comprehensive exploratory ana
 ### 2. Data Cleaning & Preprocessing ✅
 
 Implemented cleaning strategies:
+
 - **Outlier handling**: Filters out unrealistic values based on medical thresholds
 - **Categorical encoding**:
   - Gender: Binary encoding (Male=1, Female=0)
@@ -32,13 +34,15 @@ Implemented cleaning strategies:
 ### 3. Model Building ✅
 
 Logistic Regression model with:
+
 - Balanced class weights to handle class imbalance
 - Standardized features for proper coefficient interpretation
 - Predicted probabilities (risk scores as percentages)
 
 ### 4. Interpretation & Outputs ✅
 
 #### For Clinicians:
+
 - **Risk Probability**: Exact percentage (e.g., "23.5%")
 - **Top Contributing Factors**: 3-5 features with:
   - Feature name
@@ -51,6 +55,7 @@ Logistic Regression model with:
   - HIGH (>50%): Refer for diagnostic testing
 
 #### For Patients:
+
 - **Simplified Risk Category**: LOW/MODERATE/HIGH
 - **Plain Language Factors**:
   - "Your BMI is elevated"
@@ -64,13 +69,15 @@ Logistic Regression model with:
 ### 5. Visualizations ✅
 
 The web interface provides:
+
 - **Feature importance bar chart**: Displays coefficient magnitudes
 - **Risk distribution histogram**: Shows separation between diabetic and non-diabetic cases
 - **Individual factor contributions**: Diverging bar chart in clinician view
 
 ### 6. Workflow Documentation ✅
 
 The code includes:
+
 - Comprehensive comments explaining each step
 - Markdown-style documentation in this file
 - Clear function names and structure
@@ -92,6 +99,7 @@ python3 analyze.py
 ```
 
 This will:
+
 1. Generate synthetic data if `diabetes_dataset.csv` doesn't exist
 2. Clean and preprocess the data
 3. Train the logistic regression model
@@ -110,6 +118,7 @@ python3 analyze.py predict_file patient.json
 ```
 
 Output:
+
 ```json
 {
   "riskScore": 96.9,
@@ -192,6 +201,7 @@ If no dataset is provided, the script generates 1000 synthetic patient records w
 ### Data Quality
 
 The script handles:
+
 - Missing values (filters them out)
 - Unrealistic values:
   - BMI < 10 (physiologically impossible)
@@ -221,6 +231,7 @@ The Python script integrates seamlessly with the Node.js backend:
 ## References
 
 This implementation follows best practices from:
+
 - Clinical prediction models for diabetes screening (ADA guidelines)
 - Interpretable machine learning in healthcare
 - Human-AI collaboration in clinical workflows

README.md

@@ -2,7 +2,9 @@
 ![Stars](https://img.shields.io/github/stars/gopaljilab/Clinical-Insight-Engine)
 ![Issues](https://img.shields.io/github/issues/gopaljilab/Clinical-Insight-Engine)
 ![Last Commit](https://img.shields.io/github/last-commit/gopaljilab/Clinical-Insight-Engine)
-# 🩺 Clinical Insight Engine  
+
+# 🩺 Clinical Insight Engine
+
 ## Clinical Decision Support for Preventive Diabetes Risk Assessment
 
 **Clinical Insight Engine** is a full-stack clinical decision support system designed to surface early diabetes risk signals from routine patient data.  
@@ -20,7 +22,9 @@ This system is intended for **educational and research purposes only** and does
 ### 🔹 Core Functionality
 
 #### 🧾 Risk Assessment Form
+
 Inputs include:
+
 - Age, gender
 - Hypertension and heart disease status
 - Smoking history
@@ -31,21 +35,25 @@ Inputs include:
 #### 👥 Dual-View Results
 
 **Clinician View**
+
 - Exact risk percentage (0–100%)
 - Top contributing factors with impact analysis
 - Model confidence indicators
 - Suggested follow-up actions
 
 **Patient View**
+
 - Simplified risk category (**LOW / MODERATE / HIGH**)
 - Plain-language explanation of risk factors
 - Preventive lifestyle recommendations
 
 #### 🕒 Assessment History
+
 - Stores previous assessments with timestamps
 - Enables longitudinal tracking of patient risk
 
 #### 📊 Data Visualization
+
 - Interactive bar charts showing factor contributions
 - Available in clinician view for transparency
 
@@ -54,6 +62,7 @@ Inputs include:
 ## 🏗️ System Architecture
 
 ### Frontend (`client/`)
+
 - React + TypeScript
 - Vite for fast development
 - Tailwind CSS with dark mode support
@@ -63,12 +72,14 @@ Inputs include:
 - Framer Motion for animations
 
 ### Backend (`server/`)
+
 - Express.js REST API
 - PostgreSQL database via Drizzle ORM
 - Python integration for ML inference
 - Zod-based route and schema validation
 
 ### Machine Learning (`analyze.py`)
+
 - Logistic Regression (scikit-learn)
 - Feature engineering and preprocessing
 - StandardScaler for normalization
@@ -87,17 +98,20 @@ Inputs include:
 ## ⚙️ Installation & Setup
 
 ### 1️⃣ Clone the Repository
+
 ```bash
 git clone https://github.com/gopaljilab/Clinical-Insight-Engine.git
 cd Clinical-Insight-Engine
 ```
 
 ### 2️⃣ Install Node Dependencies
+
 ```bash
 npm install
 ```
 
 ### 3️⃣ Create Environment File
+
 Create a `.env` file in the project root:
 
 ```bash
@@ -109,13 +123,15 @@ Copy-Item .env.example .env
 ```
 
 If `.env.example` doesn't exist, create `.env` manually:
+
 ```env
 DATABASE_URL=postgresql://postgres:postgres@localhost:5432/clinical_insight_engine
 ```
 
 ### 4️⃣ PostgreSQL Database Setup
 
 #### Linux (Ubuntu/Debian)
+
 ```bash
 # Install PostgreSQL
 sudo apt update
@@ -131,6 +147,7 @@ sudo -u postgres psql -c "CREATE DATABASE clinical_insight_engine;"
 ```
 
 #### macOS (Homebrew)
+
 ```bash
 # Install PostgreSQL
 brew install postgresql
@@ -144,6 +161,7 @@ psql postgres -c "CREATE DATABASE clinical_insight_engine;"
 ```
 
 #### Windows
+
 1. Download PostgreSQL from [postgresql.org](https://www.postgresql.org/download/windows/)
 2. Install with default settings (remember the password for `postgres` user)
 3. Open **SQL Shell (psql)** or **pgAdmin** and run:
@@ -154,18 +172,21 @@ CREATE DATABASE clinical_insight_engine;
 ```
 
 Or via PowerShell (if `psql` is in PATH):
+
 ```powershell
 psql -U postgres -d postgres -c "CREATE DATABASE clinical_insight_engine;"
 ```
 
 ### 5️⃣ Database Migration
+
 Create the required tables:
 
 ```bash
 npm run db:push
 ```
 
 If the above doesn't work, try:
+
 ```bash
 npm run migrate
 # or
@@ -175,6 +196,7 @@ npm run db:migrate
 ### 6️⃣ Python Environment Setup
 
 #### Linux/macOS
+
 ```bash
 # Create virtual environment
 python3 -m venv .venv
@@ -190,6 +212,7 @@ pip install -r requirements.txt
 ```
 
 #### Windows (PowerShell)
+
 ```powershell
 # Create virtual environment
 py -m venv .venv
@@ -207,6 +230,7 @@ pip install -r requirements.txt
 ### 7️⃣ Dataset Preparation
 
 If dataset exists in project:
+
 ```bash
 # Linux/macOS
 cp attached_assets/diabetes_dataset.csv ./diabetes_dataset.csv
@@ -216,6 +240,7 @@ Copy-Item attached_assets/diabetes_dataset.csv ./diabetes_dataset.csv
 ```
 
 If dataset is missing, generate synthetic data:
+
 ```bash
 # Linux/macOS
 python3 -c "from analyze import create_synthetic_data; create_synthetic_data()"
@@ -227,12 +252,15 @@ py -c "from analyze import create_synthetic_data; create_synthetic_data()"
 ### 8️⃣ Start the Application
 
 #### Frontend (React + Vite)
+
 ```bash
 npm run dev
 ```
+
 Frontend runs at: `http://localhost:5173`
 
 #### ML Pipeline (Training)
+
 ```bash
 # Linux/macOS
 python3 analyze.py
@@ -242,6 +270,7 @@ py analyze.py
 ```
 
 #### Backend API (if separate)
+
 ```bash
 # Linux/macOS
 python3 main.py
@@ -257,6 +286,7 @@ py main.py
 Create a patient JSON file:
 
 #### Linux/macOS
+
 ```bash
 cat > patient.json << 'EOF'
 {
@@ -273,6 +303,7 @@ EOF
 ```
 
 #### Windows (PowerShell)
+
 ```powershell
 @'
 {
@@ -289,6 +320,7 @@ EOF
 ```
 
 Run prediction:
+
 ```bash
 # Linux/macOS
 python3 analyze.py predict_file patient.json
@@ -302,9 +334,11 @@ py analyze.py predict_file patient.json
 ## 🛑 Shutdown
 
 ### 1️⃣ Stop Development Server
+
 Press `Ctrl + C` in the terminal running `npm run dev`
 
 ### 2️⃣ Deactivate Python Environment
+
 ```bash
 # Linux/macOS
 deactivate

attached_assets/Preventive_Risk_&_Clinical_Decision_Support_1771956201814.md

@@ -1,29 +1,35 @@
 ## 🏥 Preventive Risk & Clinical Decision Support
 
 ### Theme
+
 Healthcare | Preventive Medicine | Human-AI Collaboration
 
 ### Context
+
 Chronic diseases such as diabetes often develop silently. By the time symptoms appear, interventions become costly and outcomes worsen. Clinicians operate under time pressure, limited historical data, and uncertainty—while patients struggle to understand probabilistic health risks.
 
 ### Problem
+
 Design a clinical decision support workflow that helps surface early risk signals from routine patient data and supports informed, timely interventions—without overwhelming doctors or misleading patients.
 
 ### What the Solution Should Enable
-- Transform structured patient data into risk estimates with uncertainty  
-- Identify key contributing factors and modifiable drivers  
-- Communicate findings differently for clinicians and patients  
+
+- Transform structured patient data into risk estimates with uncertainty
+- Identify key contributing factors and modifiable drivers
+- Communicate findings differently for clinicians and patients
 - Suggest next-step actions (tests, lifestyle changes, follow-ups)
 
 ### Open Design Space
+
 Teams may explore:
-- Risk scoring, stratification, or cohort discovery  
-- Counterfactual reasoning (“what would reduce risk most?”)  
-- Longitudinal patient tracking  
+
+- Risk scoring, stratification, or cohort discovery
+- Counterfactual reasoning (“what would reduce risk most?”)
+- Longitudinal patient tracking
 - Bias detection and safety considerations
 
 ### Evaluation Focus
-- Clinical relevance of insights  
-- Clarity and trustworthiness of explanations  
-- Usability in real OPD / clinic workflows  
 
+- Clinical relevance of insights
+- Clarity and trustworthiness of explanations
+- Usability in real OPD / clinic workflows