肿瘤治疗需要根据多个临床参数的测量持续进行个体化调整。利用临床数据中存在的模式的预测工具可用于协助决策并减轻与解释所有这些参数相关的负担。本研究的目标是预测胰腺癌患者在下次访问时的病情发展情况，使用在健康记录中例行记录的信息，为临床医生提供决策支持系统。我们选择血液学变量作为访问的临床结果，假设它们可以预测患者的病情发展。基于回归树的多变量模型生成用于预测所选择的每个临床结果的下次访问值，基于纵向临床数据以及从每次访问的个体患者状态的in silico模拟中流出的分子数据集。这些模型预测嗜酸性粒细胞、白细胞、单核细胞和血小板的演变趋势，平均预测得分（平衡准确度）为0.79。访问之间的时间跨度和中性粒细胞减少症是对预测演变的贡献最常见的因素之一。从系统生物学in silico模拟中包含分子变量为所选择的结果变量的观察变异提供了分子背景，主要与造血调节有关。尽管有其局限性，但本研究证明了下次访问预测工具在现实世界的应用是可行的，即使可用数据集很小。©2023 The Authors. Wiley Periodicals LLC代表美国临床药理学和治疗学会出版CPT：药物动力学和系统药理学。

Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the patterns present in the clinical data could be used to assist decision making and ease the burden associated to the interpretation of all these parameters. The goal of this study was to predict the evolution of patients with pancreatic cancer at their next visit using information routinely recorded in health records, providing a decision-support system for clinicians. We selected hematological variables as the visit's clinical outcomes, under the assumption that they can be predictive of the evolution of the patient. Multivariate models based on regression trees were generated to predict next-visit values for each of the clinical outcomes selected, based on the longitudinal clinical data as well as on molecular data sets streaming from in silico simulations of individual patient status at each visit. The models predict, with a mean prediction score (balanced accuracy) of 0.79, the evolution trends of eosinophils, leukocytes, monocytes, and platelets. Time span between visits and neutropenia were among the most common factors contributing to the predicted evolution. The inclusion of molecular variables from the systems-biology in silico simulations provided a molecular background for the observed variations in the selected outcome variables, mostly in relation to the regulation of hematopoiesis. In spite of its limitations, this study serves as a proof of concept for the application of next-visit prediction tools in real-world settings, even when available data sets are small.© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.