NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization
Aleksandr Nesterov, Bulat Ibragimov, Dmitriy Umerenkov, Artem, Shelmanov, Galina Zubkova, Vladimir Kokh

TL;DR
NeuralSympCheck introduces a neural model with logic regularization for symptom checking and disease diagnosis, outperforming existing methods especially in large, sparse decision spaces.
Contribution
It presents a novel neural approach with logic regularization that combines the strengths of Bayesian, decision tree, and reinforcement learning methods.
Findings
Outperforms existing methods in diagnosis accuracy with many symptoms and diagnoses
Effective on both real and synthetic datasets
Handles large, sparse decision spaces efficiently
Abstract
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…
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Taxonomy
TopicsMachine Learning in Healthcare · Explainable Artificial Intelligence (XAI) · Artificial Intelligence in Healthcare
