Thyroid dysfunction treatment based on deep reinforcement learning

Abstract

Thyroid dysfunction is a common disease of the endocrine system, including hyperthyroidism and hypothyroidism. The traditional diagnosis and treatment of thyroid dysfunction is highly dependent on clinical experience and static index analysis, lacking the ability of dynamic personalized management. In recent years, the application of artificial intelligence in the medical field has developed rapidly. Deep reinforcement learning provides a new way for dynamic treatment strategy by combining the representation ability of deep learning with the decision optimization framework of reinforcement learning. We propose a thyroid dysfunction treatment strategy model based on deep Q-network, which uses patient status as input and identifies abnormal physiological indicators to assess treatment outcomes, forming an intelligent decision-making system. The model employs a feedback-driven framework to simulate clinical treatment, incorporating continuous state evaluation, decision-making adjustments, and iterative optimization. Based on the Kaggle public data set, the results show that the DQN model is significantly better than baseline models (CNN and LSTM) in terms of cumulative reward, average reward and treatment goal achievement rate. This study provides an efficient and reliable data-driven protocol for the personalized treatment of thyroid dysfunction.

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