A rule-based framework for risk assessment in the health domain

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.