🏥 Predictive-Analysis: Emergency Department Outcome Prediction

This project predicts Emergency Department (ED) outcomes using machine learning (ML) models on data from MIMIC-IV-ED.

We used demographic info, vital signs, and chief complaints to predict outcomes like hospital admission, discharge, or mortality. The models help with early triage decisions and resource planning.

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📊 Problem Statement

Emergency Departments are resource-constrained and need fast decisions. Using ML models trained on structured and unstructured data, we aim to:

• Predict disposition (e.g., discharge, admit)
• Estimate mortality risk
• Improve triage support using early-available data

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🧠 Data & Tools

• Dataset: MIMIC-IV-ED v2.2 (PhysioNet)
• Tools: Python 3, Google Colab
• Libraries: Pandas, NumPy, Matplotlib, Plotly, Scikit-learn, XGBoost

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🧪 Features Used

• Demographics: age, gender, race
• Chief complaint (text, processed using NLP)
• Vital signs: temperature, heart rate, BP, SpO₂, respiratory rate
• Social factors: marital status, insurance, language
• ED details: arrival method, triage acuity, length of stay

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🛠️ ML Workflow

1. Preprocessing

   • Filter ICD-10 cases only
   • Cleaned triage, vitals, and meds tables
   • Categorical encoding + scaling
   • NLP categorization of chief complaints (reduced to 19 groups)

2. Modeling

   • Feature selection + PCA
   • K-Means clustering (4 clusters)
   • Classification models:
     • Logistic Regression
     • Decision Tree
     • XGBoost (best performer)
     • LLM (Me-LLaMA)

3. Performance

   • Best AUC-ROC: 0.88
   • Evaluated with accuracy, precision, recall, F1-score

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📌 Key Insights

• Chief Complaints + Vital Signs were the most predictive
• XGBoost outperformed all baseline models
• NLP helped convert free-text into structured inputs

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📁 Files in Repo

📦 Predictive-Analysis ├── Clustering_and_Classification.ipynb # ML model and clustering ├── ED_exploratory_analysis.ipynb # EDA & preprocessing ├── Final Report.pdf # Detailed report └── README.md # Project summary

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📬 Contact

Gaurav More
📧 more.56@buckeyemail.osu.edu
🔗 LinkedIn

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📎 Data Sources

• MIMIC-IV-ED Dataset (v2.2)
• MIMIC-IV Dataset (v3.1)