Treatment outcome prediction for Clinical Records
A multi-stage machine learning framework for stepwise prediction of tuberculosis treatment outcomes - Integrating gradient boosted decision trees and feature-level analysis for clinical decision support
Built a multi-stage machine learning model using XGBoost to predict tuberculosis treatment outcomes across different clinical checkpoints.
Integrated genomic features, patient metadata, and radiological information to support decision-making in resource-constrained healthcare settings.
Designed the framework to be robust to missing data and scalable across diverse clinical environments using TB Portals data.
Demonstrated strong predictive performance, particularly in early-stage outcome assessment, enabling prioritization and risk stratification in TB management.
Predictive accuracy across the different models
Model 1 uses only demographic features (demographics and socioeconomic data). Model 2 adds microbiological features (drug resistance and health levels), improving sensitivity and accuracy. Model 3 adds in X-ray based features (lung localisation and severity scores), further enhancing performance. Model 4 incorporates demographic, microbiological and treatment feature levels, including Treatment data (regimen and adherence), achieving the highest metrics. This stepwise progression highlights how additional diagnostic and treatment data improve predictive accuracy.