Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control. Objective: We aimed to develop a mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis. Methods: We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Internal and external validation of the predictive model was conducted in a forward-chaining setting. Results: Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment. Conclusion: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level, and could inform the design of personalised treatment strategies that can be tested in future studies. Our model-based approach can be applied to other diseases such as asthma with apparent unpredictability and large variation in symptoms and treatment responses.

中文翻译：

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使用机械式机器学习模型进行个性化的每日湿疹严重程度评分预测

背景：特应性皮炎（AD）是一种慢性炎症性皮肤病，有发作期和缓解期。鉴于个体内部和个体之间的AD症状和治疗反应明显不可预测且差异很大，因此设计AD个性化治疗策略具有挑战性。更好地预测个体患者随时间推移的AD严重程度有助于选择最佳时机和治疗类型，以改善疾病控制。目的：我们旨在开发一种机械式机器学习模型，该模型可以每天预测特定于患者的AD严重程度评分的演变。方法：我们设计了一个概率预测模型，并使用贝叶斯推理对来自两个已发表临床研究的纵向数据进行训练。数据包括每天记录的AD严重程度评分以及59和334名AD儿童在6个月和16周内使用的治疗方法。预测模型的内部和外部验证是在前向链接环境中进行的。结果：我们的模型能够在个人水平上预测未来的严重程度评分，并将机会水平的预测提高60％。使用患者特定的参数（例如AD严重程度的短期持续性以及对局部类固醇，钙调神经磷酸酶抑制剂和逐步治疗的反应性）捕获严重程度轨迹的异质性模式。结论：我们的原理证明模型成功地预测了个体严重程度AD评分的每日演变，并可以为个性化治疗策略的设计提供参考，可以在未来的研究中进行测试。