# NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization

arXiv: https://arxiv.org/abs/2206.00906 · HTML: https://mlsanity.com/paper/2206.00906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Logical Neural Networks](https://mlsanity.com/paper/2006.13155/) (2020)
- [Logic-Guided Data Augmentation and Regularization for Consistent Question Answering](https://mlsanity.com/paper/2004.10157/) (2020)
- [End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis](https://mlsanity.com/paper/1901.10623/) (2019)
- [Classification with Costly Features as a Sequential Decision-Making Problem](https://mlsanity.com/paper/1909.02564/) (2019)
- [Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference](https://mlsanity.com/paper/2012.08790/) (2020)
- [Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty](https://mlsanity.com/paper/1812.10687/) (2018)
- [BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis](https://mlsanity.com/paper/2012.01065/) (2020)
- [A Bayesian Approach for Medical Inquiry and Disease Inference in Automated Differential Diagnosis](https://mlsanity.com/paper/2110.08393/) (2021)
- [Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis](https://mlsanity.com/paper/2003.06534/) (2020)

## Built on by (most cited first, 1 of 1)

- [Variational Information Pursuit for Interpretable Predictions](https://mlsanity.com/paper/2302.02876/) (2023)

## Abstract (arXiv)

The symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. The basic symptom checking systems based on Bayesian methods, decision trees, or information gain methods are easy to train and do not require significant computational resources. However, their drawbacks are low relevance of proposed symptoms and insufficient quality of diagnostics. The best results on these tasks are achieved by reinforcement learning models. Their weaknesses are the difficulty of developing and training such systems and limited applicability to cases with large and sparse decision spaces. We propose a new approach based on the supervised learning of neural models with logic regularization that combines the advantages of the different methods. Our experiments on real and synthetic data show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.