title: A Challenge-Driven Deep Learning Framework for Antimicrobial Resistance Prediction: When Do Complex Architectures Matter?
abstract: Biomedical datasets share recurring challenges: extreme sparsity, class imbalance, high dimensionality, mixed data types, and complex inter-feature relationships. We propose a challenge-driven design philosophy where each data challenge is addressed by a dedicated architectural component, and evaluate this approach on antimicrobial resistance (AMR) prediction — a critical clinical problem where traditional susceptibility testing requires 48–72 hours. Our framework maps five challenges to architectural solutions: (1) sparse binary features to a Bernoulli encoder providing gradient flow despite >94% zeros; (2) zero-inflated continuous features to a Zero-Inflated MLP encoder; (3) complex feature relationships to a heterogeneous graph neural network with five semantic edge types; (4) multi-faceted information integration to type-specific Graph Attention Networks with attention-based fusion; (5) class imbalance to Dynamic Task Prioritization loss. Evaluated on the AMR-UTI dataset (116,902 cases), all models, including simple baselines, achieved AUROC of 0.68–0.83 across four antibiotics, representing 12.5–17.7% improvement over prior graph-based methods. Critically, comprehensive evaluation across three experimental regimes (full features, dominant feature removal, and temporal distribution shift from 2007–2013 to 2014–2016) revealed that a standard multi-layer perceptron achieves equivalent performance in all regimes, indicating that improvement stems from preprocessing and training methodology rather than architectural complexity. Feature importance analysis identified colonization pressure and prior resistance as dominant predictors that obviate relational modeling. We propose a signal-dominance framework explaining when complex architectures are unnecessary, thus, contributing to the understanding of architectural selection in clinical machine learning.
language of the presentation: English
|