NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization
This addresses the need for more accurate and scalable medical diagnostic systems, though it appears incremental as it builds on existing neural and logic-based approaches.
The paper tackles the problem of symptom checking and disease diagnosis by proposing a neural model with logic regularization, which outperforms existing methods in diagnostic accuracy when dealing with large numbers of diagnoses and symptoms.
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.