NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization (2206.00906v1)

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 paper presents a novel NeuralSympCheck model that outperforms existing systems in diagnostic prediction accuracy.
• It employs a two-part architecture with logic regularization, replacing complex RL methods and reducing computational demands.
• Extensive experiments and ablation studies validate its robustness and practical applicability in real-world diagnostics.

Enhancing Diagnostic Accuracy with a Novel Symptom Checker Model: A Logic Regularization Approach

Introduction

The healthcare industry constantly seeks to optimize the balance between accessibility, quality, and cost, often referred to as the "iron triangle." Against this backdrop, AI-driven symptom checker systems have emerged as a promising tool to aid in self-diagnosis processes, offering a step-up from traditional search-engine based symptom lookup. This paper pioneers a new symptom checker model, NeuralSympCheck, deploying a novel logic regularization framework faring better than existing methods, especially in handling large symptom and diagnosis spaces efficiently.

The Symptom Checker Model

The NeuralSympCheck model consists of two parts: a symptom suggestion submodel and a diagnosis prediction submodel, trained end-to-end with logic regularization. This method reframes symptom recommendation as a multi-label classification task, employing Asymmetric loss to efficiently manage the vast and sparse symptom space. The paper distinguishes itself by bypassing the complications inherent to reinforcement learning (RL)-based systems, presenting an approach not only simpler in deployment but also in computational demands.

Key Contributions

• The NeuroSympCheck model outperforms current state-of-the-art systems in diagnosis prediction accuracy across both real-world and synthetic datasets.
• Logic regularization replaces the RL framework, simplifying the training process and offering independence from the order of symptoms presented.
• The system exemplifies a practical application, with less computational resource requirement and simplicity in training.

Methodology

The methodology section explores the intricate architecture of the NeuralSympCheck model. The symptom suggestion submodel functions by assessing present and absent symptoms, subsequently suggesting the next most probable symptom to inquire about. The diagnostics submodel utilizes this information to predict the disease. Through the logic regularization process, both models are trained simultaneously, enabling an efficient feedback loop that enhances the model’s diagnostic accuracy. Moreover, the paper discusses an approach to quantify the uncertainty of diagnosis predictions, establishing a mechanism to decide when additional symptom information is likely redundant.

Experimental Evaluation

The paper includes a thorough experimental evaluation, showcasing the model's superior performance over a range of datasets. NeuralSympCheck demonstrates remarkable accuracy improvements in diagnosis prediction against current RL-based and other symptom checker models. Particularly notable are the results from the synthetic dataset, SymCat, where the model consistently excels across variants with different numbers of diseases, reflecting its robustness in handling complex diagnostic scenarios.

Ablation Studies and Uncertainty Estimation

A series of ablation studies further validate the significance of both the symptom prediction loss methodology and the incorporation of uncertainty estimation. These investigations affirm that the logic regularization approach not only contributes to the model's high diagnostic prediction accuracy but also facilitates a judicious symptom inquiry process, reminiscent of real-life diagnostic reasoning.

Conclusion and Future Directions

The paper introduces a cutting-edge NeuralSympCheck model that sets a new benchmark for symptom checker systems. By melding supervised learning with logic regularization, the paper addresses the limitations of preceding models and unfolds a pathway towards more accessible and accurate diagnostic tools. With its practical advantages, including ease of implementation and lower computational demands, the NeuralSympCheck model holds promise for integration into real-world medical systems, marking a significant stride towards ameliorating the diagnostic process in the digital age. Future endeavors will likely focus on refining this model further, exploring its potential in diverse healthcare settings, and extending its applicability to a broader spectrum of diseases and symptoms.