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 best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large. The models and the code are freely available online (https://github.com/SympCheck/NeuralSymptomChecker).
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We are grateful to anonymous reviewers for their valuable feedback. The work was supported by the RSF grant 20-71-10135.
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Appendices
A Dataset Statistics and Hyperparameters
B Additional Experimental Results
Change of entropy value depending on iteration of symptom inquiring
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Nesterov, A., Ibragimov, B., Umerenkov, D., Shelmanov, A., Zubkova, G., Kokh, V. (2022). NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_8
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