# Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation
## 5 Medical Consultation Flows
### Selecting an Already Defined Case Type
\[similarity(CT_{1},CT_{2})=\sum_{sec=sec_{1}}^{sec_{1}}w_{sec}\times similarity CC_{sec} \tag{14}\] \[=\sum_{sec=sec_{1}}^{sec_{2}}w_{sec}\times similarityCC_{sec}+ \sum_{sec=sec_{1}+1}^{sec_{N}}w_{sec}\times similarityCC_{sec}\] \[\leq\sum_{sec=sec_{1}}^{sec_{2}}w_{sec}\times similarityCC_{ sec}+\sum_{sec=sec_{2}+1}^{sec_{N}}w_{sec}=\sum_{sec=sec_{2}}^{sec_{2}}w_{sec} \times similarityCC_{sec}+R_{constant}^{k+1}\]

Equation (15) presents an upper bound inferred by simplifying (14), which can be used for discarding case types with low similarity values. \[similarity(CT_{1},CT_{2})\leq\sum_{sec=sec_{1}}^{sec_{2}}w_{sec}\times similarity CC_{sec}+R_{constant}^{k+1} \tag{15}\]

Several component similarity values (_similarity_CCs) need to be computed to obtain the final value of _similarity_(\(CT_{1}\), \(CT_{2}\)). An upper bound is identified after determining the value of _similarity_CC\({}_{sec_{1}}\). After calculating the similarity of the \(k\)th conceptual component, it is possible to use an upper bound to discard a case type with a low similarity value. Each case type whose upper bound of similarity is lower than the worst element of the top best metrics is considered a non-promising candidate, and no more computational effort is expended to calculate its final similarity value. ## 7 Experimental Validation

This section presents the experimental validation of the proposed approach on a real case study, which served as a basis for evaluating the practical aspect of this research. ### Problem Instances

The source _Clinical cases in primary care_[18] was used for evaluating the proposed approach. The source is a multi-authored publication that covers a wide range of clinical scenarios of primary care. #### 7.1.1 Prerequisites for Building Case Type Instances

The collaboration of advanced medical students was requested with the intention of registering as many clinical scenarios as possible. Students were instructed to record the primary care scenarios described in _Clinical cases in primary care_ as new clinical CTs. In order to group all the information recorded, it was necessary to implement procedures for exchanging clinical CTs. The _export_ and _import_ procedures were used to exchange different CTs. A procedure to _export_ a given CT was implemented. The procedure extracts a CT from a specific clinical knowledge base CKB, and it also anonymizes any information that refers to the person who wrote (owner) the CT. The _import_ procedure consolidates the information of a specific CT into a target CKB. A new CT is inserted into the target CKB, replacing any anonymized reference of the original owner with the person who owns the target CKB. Importing a CT is a complex procedure, which must avoid the generation of duplicate units of thought, and has to merge the conceptual elements of the new CT with those existing in the target CKB. #### 7.1.2 Building Case Type Instances

The set of clinical cases specified in the publication _Clinical cases in primary care_ was distributed to be evaluated by 50 advanced medical students. Each student had to evaluate three different cases, and each clinical case was assigned to at least one student. Furthermore, each student was instructed to contribute two additional clinical cases, defined as variants of those presented in the clinical source. All scenarios of primary care detailed in the clinical source were successfully registered by the group of students, including variants of repeated clinical scenarios. Algorithm 11 details how a single CKB was loaded with 250 scenarios of primary care, based on information registered by students.