https://github.com/jhaayush2004/novartis

National Winning Solution at NEST hackathon : Predicting Recruitment Rate in Clinical Trials
https://github.com/jhaayush2004/novartis

Last synced: 3 months ago
JSON representation

National Winning Solution at NEST hackathon : Predicting Recruitment Rate in Clinical Trials

• Host: GitHub
• URL: https://github.com/jhaayush2004/novartis
• Owner: jhaayush2004
• License: mit
• Created: 2025-03-11T17:26:46.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-11T17:45:30.000Z (3 months ago)
• Last Synced: 2025-03-11T18:30:26.628Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 195 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Predicting Recruitment Rate in Clinical Trials

## National Winning Solution at NEST hackathon out of 32000+ registrations across India.

## Overview

This project addresses the problem of predicting the Study Recruitment Rate (RR) for clinical trials, a critical aspect of the drug development process. By implementing a structured approach leveraging advanced machine learning techniques and large language models, this solution provides actionable insights to optimize clinical trial recruitment strategies.

## Key Highlights

- *Objective*: Predict Recruitment Rate (RR) using structured and textual data.

- *Tools and Frameworks*:

  - *Transformers*: Used BioBERT for extracting semantic embeddings from textual data.

  - *PyTorch*: For GPU-accelerated computations.

  - *Scikit-learn*: For Gradient Boosting Model (GBM) training and evaluation.

  - *Bayesian Optimization*: Hyperparameter tuning using bayes_opt.

  - *Google Colab*: For GPU-enabled computation.

  - *Matplotlib & Seaborn*: Data visualization.

  - *Pandas & NumPy*: Data preprocessing and numerical computations.

## Methodology

1.  *Data Preprocessing*:

    *   Handled missing values and irrelevant columns.

        *   Strategically addressed missing values to prevent data loss, employing techniques like imputation with mean/median for numerical columns or mode for categorical ones, depending on the data distribution, thus preserving valuable information.

        *   For columns with excessive missingness, considered dropping them to avoid introducing bias through imputation, acknowledging the trade-off between data quantity and quality.

    *   Transformed categorical features to numerical representations (e.g., one-hot encoding).

        *   Transformed categorical features into numerical representations using one-hot encoding, creating binary columns for each category and enabling the model to process categorical data effectively.

        *   Applied label encoding for ordinal categorical features, mapping categories to numerical values based on their inherent order, capturing the ordinal relationship for the model.

    *   Extracted embeddings for textual features using BioBERT.

        *   Extracted contextual embeddings for textual features using BioBERT, a pre-trained language model, capturing semantic relationships and nuances within the text data.

        *   Fine-tuned BioBERT embeddings to tailor them specifically to the task, enhancing the model's ability to understand and utilize textual information for improved performance.

    *   Standardized numerical columns for uniform scaling.

        *   Standardized numerical columns using StandardScaler, transforming data to have zero mean and unit variance, ensuring uniform scaling and preventing features with larger ranges from dominating the model.

        *   Alternatively, employed MinMaxScaler to scale numerical features to a specific range (e.g., 0 to 1), preserving the original distribution and handling outliers effectively.

    *   **Outlier Handling:**

        *   Identified and addressed outliers using methods like IQR-based filtering or Z-score analysis, mitigating their impact on model training and improving generalization performance.

        *   Applied transformations like log or square root to reduce the skewness of data caused by outliers, making the data distribution more normal and stabilizing variance.

    *   **Class Imbalance Handling:**

        *   Tackled class imbalance issues using techniques like oversampling the minority class (e.g., SMOTE) or undersampling the majority class, creating a more balanced dataset and preventing the model from being biased towards the majority class.

    *   **Feature Engineering:**

        *   Created new features by combining or transforming existing ones, capturing complex relationships within the data and providing the model with additional predictive signals.

        *   Generated interaction features by multiplying or combining different columns, allowing the model to capture non-linear relationships between features and improve accuracy.

    *   **Data Type Conversion:**

        *   Ensured proper data types for each feature, converting them if necessary (e.g., numeric to category) to optimize memory usage and improve computational efficiency.

    *   **Data Transformation**

        *   Applying different mathematical transformation to see the model performace.

2. *Model Training*:

   - Utilized GBM for robust regression, optimized using Bayesian techniques.

   - Compared results with LightGBM as a benchmark.

   - Applied stratified train-test splitting to maintain data consistency.

3. *Evaluation Metrics*:

   - *Root Mean Square Error (RMSE)*: 0.34

   - *Mean Absolute Error (MAE)*: 0.083

   - *R² Score*: 0.45

   - Utilized SHAP for model explainability and feature importance analysis.

## Results

- *Key Features*:

  - Duration of trial, enrollment, and primary completion time were identified as critical predictors.

- *Insights*:

  - Low RMSE and MAE indicate high accuracy and consistency.

  - Explainable AI techniques like SHAP enhance trust in model predictions.

## Challenges

- *Hardware Limitations*: Limited access to high-performance GPUs restricted experimentation with advanced models like GPT-4.

- *Data Imbalance*: Skewed recruitment rates posed challenges in maintaining generalizability.

## Next Steps

- Explore dynamic feature selection using reinforcement learning.

- Fine-tune larger LLMs like LLaMA-3.3 for improved embeddings.

- Implement continuous learning frameworks for model updates with new data.

- Address temporal dynamics using advanced time-series models.

## Lead Members

-  Ayush Shaurya Jha(Mentor & Lead) - IIIT Ranchi

-  Satyam kumar(Lead) - IIT Kanpur

  

## Acknowledgments

The project utilized insights from academic papers and was powered by an NVIDIA A100 GPU through OLA Krutrim.

## References

- [BioBERT Research Paper](https://academic.oup.com/bioinformatics/article/36/4/1234/5566506)

- [Recruitment Rate Insights](https://trialhub.com/resources/articles/clinical-trial-recruitment-rate-4-things-to-know)