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Abstract：
Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare.
摘要：
机器学习（ML）已用于预测溃疡性结肠炎（UC）中生物制剂的功效。ML可以提供精度，个性化,效率,和自动化。此外，它可以改善预测临床结局的决策支持。然而,它面临着与数据质量和数量相关的挑战，过拟合,泛化，和可解释性。本文评论了两种最新的ML模型，这些模型预测了维多珠单抗和ustekinumab在UC中的疗效。考虑多种途径的模型，多种族,现实世界和临床试验数据的组合是最佳的共享决策和精准医学所必需的。本文还强调了将ML与计算模型相结合以增强临床结果和个性化医疗保健的潜力。关键见解：（1）ML提供精度，个性化,效率,和决策支持，预测生物制剂在UC中的疗效。(2)ML预测中具有挑战性的方面包括数据质量，过拟合,和可解释性。（3）多种途径，多种族,和现实世界和临床试验数据的组合应考虑在预测模型的最佳决策。（4）将ML与计算模型相结合可以改善临床结果和个性化医疗保健。
