Medical Appointment No-Show Analysis

Business Impact and Project Importance

Medical appointment no-shows lead to significant inefficiencies in healthcare systems, including wasted resources, lost revenue, and compromised patient outcomes. By accurately predicting and understanding no-show behavior, healthcare providers can optimize scheduling, improve resource utilization, and enhance patient care. This project addresses these challenges through an end-to-end analytical framework that leverages:

• Advanced Feature Engineering: Extracts and transforms temporal, categorical, and numerical data to enhance predictive power.
• Unsupervised Learning: Utilizes clustering (KModes and HDBSCAN) to uncover latent group structures in patient behavior.
• Bayesian Optimization & Advanced Modelling Techniquesx: Fine-tunes predictive models like XGBoost and AutoTabPFN for improved performance.
• Robust Causal Inference: Employs Double Machine Learning and meta-learning techniques to estimate the impact of SMS reminders on no-show rates.

Key Business Benefits:

• Operational Efficiency: Reducing no-shows improves appointment scheduling and resource allocation.
• Revenue Optimization: Fewer missed appointments lead to better financial performance.
• Enhanced Patient Outcomes: Targeted interventions increase appointment adherence and overall care quality.
• Data-Driven Strategies: Provides actionable insights for designing effective patient engagement programs.

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Project Overview

This project is structured around three main components:

1. Feature Engineering and Unsupervised Learning

   • Techniques:
     • Temporal feature extraction from scheduling and appointment timestamps.
     • Binary encoding of categorical variables.
     • Log transformation to normalize skewed numerical features.
     • Clustering:
       • KModes clustering to capture categorical groupings.
       • HDBSCAN with Bayesian optimization to adapt to varying data densities.
   • Learn More:
     Feature Engineering & Clustering Report

2. Advanced Modeling with Bayesian Optimization

   • Techniques:
     • Propensity score estimation using Logistic Regression.
     • XGBoost modeling optimized via Bayesian hyperparameter tuning.
     • Deployment of advanced models such as TabPFN and AutoTabPFN.
   • Outcome:
     Highly optimized predictive models that accurately identify patients at risk of no-shows.
   • Learn More:
     Modeling & Predictive Analytics Report

3. Causal Inference Analysis

   • Techniques:
     • Double Machine Learning approaches (LinearDML, CausalForestDML) for estimating average treatment effects.
     • Meta-learners (T-Learner, X-Learner, S-Learner) to capture heterogeneous treatment effects.
     • Validation techniques including placebo tests and SHAP analysis for model interpretation.
   • Outcome:
     Robust estimates of the causal impact of SMS reminders on reducing no-show rates.
   • Learn More:
     Causal Inference Analysis Report

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To evaluate this project, please review the reports in the "reports" folder, starting with Part 1 through Part 3. The corresponding notebooks are linked in the "notebooks" folder for your convenience.