## 2 Results and Discussion
### Use of Information Models, Classifications and Terminologies in Rehabilitation  
Regarding the use of **information models** in rehabilitation _CDSSs_, vMR is still in process of improvement and evolution by VMR Project Team [10], and there are several CDSSs that use it [11]. Moreover, VMR is designed to reduce development costs and time responses in CDSSs. As a consequence, although it is not widely implemented in hospitals and there are not any tools available to facilitate its implementation (unlike the information model EN/ISO 13606 [12]), it is probably the most appropriate information model for CDSSs today.  
Several **classifications** and **termininologies** are used in CDSSs. In rehabilitation, ICF is used for encoding patients' health status and professionals' _recommendations_. Recommendations are activities and changes in environmental factors suggested by professionals to improve the quality of life of the patient. ICD-10 is used for representing diseases and SNOMED CT for other attributes; in particular, SNOMED CT has the potential of getting ICF-related terms mainstreamed in clinical systems, providing more context when necessary and including semantic relationships.  
In rehabilitation processes, several questionnaires are used, which evaluate functioning, disability and health. In order to solve interoperability problems among questionnaires, items can be encoded to ICF concepts following the standardization methodology proposed by [PERSON] _et al._ (2005) [13]. Difficulties in mapping clinical questionnaires to standard terminologies and ontologies in the rehabilitation domain are summarized by [PERSON] and [PERSON] (2012) [14]; e.g., data from questionnaires have usually a finer granularity than ICF _core set_ categories. That is the reason why, to _summarize the health status_ using ICF _core sets_ from data obtained via questionnaires administered in the PIE (see examples of questionnaires in Section 3.2), the value of some (more general) nodes of the ICF taxonomy needs to be computed from the (more specific) node's children. This process is therefore performed in a bottom-up fashion and only the nodes with values are considered in the computation.  
Several aggregating functions can be used depending on the attribute and the domain. In the physical rehabilitation domain, the average function is usually used. Another example is the _pessimistic function_, where if parents have an empty value, the parents' value is computed from the worst value of their children (ICF scale ranges from _4 = complete problem_ to _0 = no problem_).  
The CDSS, whose architecture is shown in Figure 1, uses standard information models, classifications and terminologies. Data from questionnaires administered in the PIE are stored in an _electronic medical record_ (EMR) and a vMR reader/writer converts the EMR into vMR format. The health status obtained from vMR is represented in the knowledge base in the OWL ontology language [14]. A Java library converts original data into standardized data. A selection of instances of the knowledge base is stored in the case base, which, together with the ICF and ICD-10, is used to find similar patients via an open source CBR framework: jColibri [15]. Patients' health status information is summarized by another Java library which uses the ICF and is accessed through an Internet-based interface.