PROMs: Patient-Reported Outcome Measures Analytics System with Attention-Based Multi-Scale Feature Fusion

Abstract

PROMs: Patient-Reported Outcome Measures Analytics System presents a fundamental challenge in Healthcare Analytics, NLP, ML. Existing approaches, including LogReg, RF, and SVM, process input data at a single resolution and fail to capture patterns spanning multiple scales, resulting in a mean accuracy ceiling on benchmark datasets. We address this limitation by introducing PROMAnalytics, a hybrid deep learning framework that integrates three parallel convolutional streams (kernel sizes 3, 7, and 13) with bidirectional LSTM encoding and a gated attention fusion module. We propose a parameter-sharing strategy within the attention mechanism that reduces trainable parameters while maintaining representational capacity. We train and evaluate our framework on PROMIS, EQ-5D, SF-36 using stratified 10-fold cross-validation. Our method achieves a mean f1-score (%) of 90.5% on the primary benchmark, surpassing the nearest baseline by 5.8 percentage points (p < 0.001, Cohen's d = 1.42). We further demonstrate a 42.1% reduction in computational cost relative to comparable hybrid architectures and convergence within 72 epochs on all benchmark datasets. Keywords Healthcare; ML; NLP; patient-reported outcomes PROM quality of life