# Optimizing Binary Symptom Checkers via Approximate Message Passing

arXiv: https://arxiv.org/abs/2111.00303 · HTML: https://mlsanity.com/paper/2111.00303/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 30 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning](https://mlsanity.com/paper/1903.03495/) (2019)
- [Approximate message-passing for convex optimization with non-separable penalties](https://mlsanity.com/paper/1809.06304/) (2018)

## Abstract (arXiv)

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.