Optimizing Binary Symptom Checkers via Approximate Message Passing

TL;DR

This paper applies approximate message passing algorithms within a compressive sensing framework to optimize binary symptom checkers, addressing non-convex inference challenges and improving performance in e-healthcare applications.

Contribution

It introduces a novel formulation of symptom checking as a non-convex optimization problem and demonstrates the effectiveness of G-VAMP algorithm for binary symptom inference.

Findings

G-VAMP outperforms other algorithms in symptom checking accuracy

Formulation as a non-convex optimization problem is effective

Enhances performance of binary symptom checkers in healthcare

Abstract

Symptom checkers have been widely adopted as an intelligent e-healthcare application during the ongoing pandemic crisis. Their performance have been limited by the fine-grained quality of the collected medical knowledge between symptom and diseases. While the binarization of the relationships between symptoms and diseases simplifies the data collection process, it also leads to non-convex optimization problems during the inference step. In this paper, we formulate the symptom checking problem as an underdertermined non-convex optimization problem, thereby justifying the use of the compressive sensing framework to solve it. We show that the generalized vector approximate message passing (G-VAMP) algorithm provides the best performance for binary symptom checkers.