Broad Learning for Healthcare

TL;DR

This paper explores broad learning techniques to integrate diverse healthcare data types, aiming to improve disease understanding and personalized treatment through advanced data fusion and analysis methods.

Contribution

It introduces novel methods for multi-view feature selection, subgraph pattern mining, brain network embedding, and sequence prediction tailored for healthcare data integration.

Findings

Enhanced disease diagnosis accuracy

Improved brain connectivity analysis

Effective multi-modal data fusion

Abstract

A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital footprints recorded on smart sensors). Capability for modeling information from these heterogeneous data sources is potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Our works in this thesis attempt to facilitate healthcare applications in the setting of broad learning which focuses on fusing heterogeneous data sources for a variety of synergistic knowledge discovery and machine learning tasks. We are generally interested in computer-aided diagnosis, precision medicine, and mobile health by creating accurate user profiles which include important biomarkers, brain connectivity patterns, and latent representations. In particular, our works involve four different data mining problems with application to the healthcare domain: multi-view feature selection, subgraph pattern mining, brain network embedding, and multi-view sequence prediction.