Kamimura Hiroteru, Nonaka Hirofumi, Mori Masaya, Kobayashi Taichi, Setsu Toru, Kamimura Kenya, Tsuchiya Atsunori, Terai Shuji
Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Department of Network Medicine for Digestive Diseases, Niigata University School of Medicine, Niigata 951-8510, Japan.
J Clin Med. 2022 Jan 13;11(2):387. doi: 10.3390/jcm11020387.
Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes.
深度学习是机器学习的一个子集，可用于准确预测生物学转变。消除乙型肝炎表面抗原（HBsAg）是慢性乙型肝炎的最终治疗终点。目前尚未建立HBsAg水平消失或降低的可靠预测指标。准确的预测对于成功治疗至关重要，全球范围内都在为此做出相应努力。因此，本研究旨在确定一种最佳深度学习模型，以预测非活动性携带者患者日常临床实践中HBsAg水平的变化。我们确定了那些HBsAg水平经过10年评估的患者。获取了常规肝脏生化功能检查结果，包括1年、2年、5年和10年的血清HBsAg水平以及生物特征信息。90例患者的数据被纳入适应性训练。基于索尼神经网络控制台设置的算法构建预测模型，并使用统计分析比较其准确性。多元回归分析显示平均绝对百分比误差为58%，而深度学习显示平均绝对百分比误差为15%；因此，深度学习是一种准确的预测判别工具。本研究证明了深度学习算法预测临床结果的潜力。
