BACKGROUND Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. RESULTS In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. CONCLUSIONS By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

中文翻译：

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将数学模型预测集成到常规工作流程中，以支持血液学临床决策。

背景技术个性化和针对患者的治疗优化是现代卫生保健的主要目标。实现此目标的一种方法是高分辨率诊断的应用以及靶向疗法的应用。然而，越来越多的不同治疗方式也带来了新的挑战：尽管随机临床试验侧重于证明特定患者组的平均治疗效果，但是在单个患者水平上的直接结论是有问题的。因此，确定最佳的针对患者的治疗方案仍然是一个悬而未决的问题。系统医学，特别是机械数学模型，可以充分支持个体治疗的优化。除了提供对疾病机制和治疗效果的更好的一般理解之外，这些模型可以识别特定于患者的参数，因此可以针对不同治疗方式的效果提供个性化的预测。结果在下面，我们描述一个软件框架，该软件框架有助于将数学模型和计算机模拟集成到常规临床过程中以支持决策。这是通过将标准数据管理和数据探索工具与针对个别患者级别的治疗方案的数学模型预测的生成和可视化相结合来实现的。结论通过以审计追踪兼容的方式将模型结果集成到已建立的临床工作流程中，我们的框架具有促进在临床实践中使用系统医学方法的潜力。