Inspiration
FibroPred was inspired by the urgent need to improve prognostic predictions for pulmonary fibrosis, a life-threatening disease with uncertain outcomes. Seeing the challenges clinicians face due to the lack of precise prediction tools motivated us to create a solution that could guide treatment decisions from the start.
What it does
FibroPred is an AI-powered predictive tool that forecasts the progression of pulmonary fibrosis from the point of diagnosis. It leverages machine learning algorithms to provide personalized risk assessments and treatment recommendations, ensuring better-informed clinical decisions.
How we built it
- Data Collection & Preprocessing: We gathered anonymized patient data, ensuring adherence to medical privacy standards. Relevant clinical features like age, lung function, and genetic markers were selected and processed.
- Model Development: We used XGBoost, optimizing it for three stages: diagnosis, one-year follow-up, and two-year follow-up. We integrated SHAP-based explainability to make model predictions transparent and interpretable.
- Web Interface: We developed an intuitive web platform featuring data input, real-time predictions, and model retraining capabilities.
- Testing & Validation: We tested the model using historical patient data, refining it based on expert feedback.
Challenges we ran into
-Understanding Relevant Variables: We needed to fully understand and select relevant variables by studying the medical context, ensuring our model captured essential clinical factors.
- Model Accuracy vs. Interpretability: Balancing the model's predictive power with the need for transparent, explainable results.
- User Experience: Creating an interface simple enough for healthcare professionals to use efficiently.
Accomplishments that we're proud of
- Understanding Medical Context: We deepened our knowledge of the background and context of patients suffering from pulmonary fibrosis, gaining insights into the diagnostic process doctors follow.
- Model Explainability: Successfully integrating SHAP for clear, interpretable predictions.
What we learned
- Interdisciplinary Collaboration: Working closely with medical experts was crucial for clinical relevance.
- Data Processing: We improved our understanding of handling complex medical datasets securely.
- Model Development: We learned how to balance accuracy with transparency to build trust in our AI system.
What's next for FibroPred: Precisió Predictiva en l'Atenció Pulmonar.
- Including More Patient Data: We plan to incorporate data from many more patients to improve accuracy, especially at the first stage of diagnosis.
- Adopting Advanced Architectures: We aim to transition to more complex deep learning models capable of identifying intricate relationships within the data.
- Seamless Integration: Supporting real-time data updates and enabling integration into hospital systems worldwide to set a new standard for personalized pulmonary care through predictive precision and explainability.
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