Abstract Risk assessment is an important decision support task in many domains, including health, engineering, process management, and economy. There is a growing interest in automated methods for risk assessment. These methods should be able to process information efficiently and with little user involvement. Currently, from the scientific literature in the health domain, there is availability of evidence-based knowledge about specific risk factors. On the other hand, there is no automatic procedure to exploit this available knowledge in order to create a general risk assessment tool which can combine the available quantitative data about risk factors and their impact on the corresponding risk. We present a Framework for the Assessment of Risk of adverse Events (FARE) and its first concrete applications FRAT-up and DRAT-up, which were used for fall and depression risk assessment in older persons and validated on four and three European epidemiological datasets, respectively. FARE consists of i) a novel formal ontology called On2Risk; and ii) a logical and probabilistic rule-based model. The ontology was designed to represent qualitative and quantitative data about risks in a general, structured and machine-readable manner so that this data may be concretely exploited by risk assessment algorithms. We describe the structure of the FARE model in the form of logic and probabilistic rules. We show how when starting from machine-readable data about risk factors, like the data contained in On2Risk, an instance of the algorithm can be automatically constructed and used to estimate the risk of an adverse event.

中文翻译：

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基于规则的健康领域风险评估框架

摘要 风险评估是许多领域的重要决策支持任务，包括健康、工程、过程管理和经济。人们对风险评估的自动化方法越来越感兴趣。这些方法应该能够有效地处理信息并且几乎不需要用户参与。目前，从健康领域的科学文献中，可以获得关于特定风险因素的循证知识。另一方面，没有自动程序可以利用这些可用知识来创建通用风险评估工具，该工具可以结合有关风险因素及其对相应风险的影响的可用定量数据。我们提出了一个不良事件风险评估框架 (FARE) 及其第一个具体应用 FRAT-up 和 DRAT-up，用于老年人跌倒和抑郁风险评估，并分别在四个和三个欧洲流行病学数据集上进行了验证。FARE 包括 i) 一个名为 On2Risk 的新型形式本体；和 ii) 基于逻辑和概率规则的模型。本体旨在以一般、结构化和机器可读的方式表示有关风险的定性和定量数据，以便风险评估算法可以具体利用这些数据。我们以逻辑和概率规则的形式描述了 FARE 模型的结构。我们展示了如何从有关风险因素的机器可读数据（如 On2Risk 中包含的数据）开始，如何自动构建算法实例并用于估计不良事件的风险。