Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness
This addresses a practical problem in healthcare analytics for improving patient outcomes by enhancing multi-view data utilization, though it is incremental as it builds on existing diffusion and multi-view methods.
The paper tackles the challenges of random missing views and view laziness in multi-view Electronic Health Record (EHR) data for healthcare predictions by introducing Diffmv, a diffusion-based generative framework that integrates views into a unified diffusion-denoising process and uses a reweighting strategy, achieving superior performance on multiple tasks across three datasets.
Advanced healthcare predictions offer significant improvements in patient outcomes by leveraging predictive analytics. Existing works primarily utilize various views of Electronic Health Record (EHR) data, such as diagnoses, lab tests, or clinical notes, for model training. These methods typically assume the availability of complete EHR views and that the designed model could fully leverage the potential of each view. However, in practice, random missing views and view laziness present two significant challenges that hinder further improvements in multi-view utilization. To address these challenges, we introduce Diffmv, an innovative diffusion-based generative framework designed to advance the exploitation of multiple views of EHR data. Specifically, to address random missing views, we integrate various views of EHR data into a unified diffusion-denoising framework, enriched with diverse contextual conditions to facilitate progressive alignment and view transformation. To mitigate view laziness, we propose a novel reweighting strategy that assesses the relative advantages of each view, promoting a balanced utilization of various data views within the model. Our proposed strategy achieves superior performance across multiple health prediction tasks derived from three popular datasets, including multi-view and multi-modality scenarios.