Robust training of recurrent neural networks to handle missing data for disease progression modeling

Understand

Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and monitoring of disease.

• Existing DPM algorithms neglect temporal dependencies among measurements and make parametric assumptions about biomarker trajectories.
• In addition, they do not model multiple biomarkers jointly and need to align subjects' trajectories.
• In this paper, recurrent neural networks (RNNs) are utilized to address these issues.