# Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation
## 4 Clinical Knowledge Model to Follow Physician Reasoning
### Clinical Knowledge Base
#### 4.1.1 Unit of Thought  
As defined by [PERSON] [15], a unit of thought is a statement that describes a basic clinical idea. Let \(UT^{M}\) be a unit of thought registered by physician \(M\). \(UT^{M}\) is denoted as \(UT^{M}=<\)_ptext_, \(u\)_qcn_, \(u\)_qpt_, _exph_, _terms_, _inuse_, _ctSchedule_, \(M\)\(>\), where _ptext_ denotes a string capable of containing structured or random data, \(u\)_qcn_ indicates if the unit refers to information to be used only in a unique consultation, \(u\)_qpt_ indicates if the unit refers to unique information of a specific patient, _exph_ indicates if the unit contains exclusive data for physician use, _terms_ detail associations with health terminological standards, _inuse_ denotes if the unit is in use during a consultation, and _ctSchedule_ indicates the frequency that a unit appears in a case type. A unit of thought used in a case type will reappear each time the case type is used, unless a specific frequency is defined by its _ctSchedule_ attribute. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Work** & **Method(s)** & **Relevant Features** & **Weaknesses** \\ \hline [PERSON] et al. [6] & Ad hoc patient similarity algorithm. & Menu of inferred recommendations, real-time feedback. & Only considers structured data. \\ \hline [PERSON] et al. [7] & Bayesian networks. & Suggest initial drafts, reduce workload of physicians. & Relies on a small set of orders and diagnoses. \\ \hline [PERSON] et al. [8] & Linear support vector machine. & Use information from previous practices, high precision for common cases. & Low precision when dealing with less common cases. \\ \hline [PERSON] et al. [9] & Natural language processing, support vector machine classifier. & Medical structured-related concept model, detect patterns about patient health. & Only evaluated on clinical phrases with more than one medical concept. \\ \hline [PERSON] et al. [10] & Language analysis, ad hoc matching. & Suggest diagnoses, prognosis and treatments. & Knowledge representation fails to analyze evolutionary contexts. \\ \hline [PERSON] et al. [11] & Preprocessing, machine learning techniques. & Reduce error-prone steps during diagnostics, user-friendliness. & Variable length array types not supported, not useful for longitudinal data capture. \\ \hline [PERSON] [12] & Cost-sensitive support vector machine. & Web services with learning capabilities, generate decision rules. & Only acceptable accuracy values of decision rules. \\ \hline [PERSON] et al. [13] & Rules for generic cases, case-based reasoning component. & Adaptive questionnaire according to patient profile. & No prototype was implemented. \\ \hline [PERSON] et al. [14] & Actionable graphs. first-order logic. & Clinical guidelines for multi-morbidity conditions, considers patient preferences. & No real evaluation. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of reviewed works. The set of all units of thought registered by physician \(M\) is denoted as \(UT_{T}^{M}\). Let \(UT_{T}^{M}\) = \(<\)_ptext\({}_{1}\)_, \(u\)_q_\({}_{1}\)_, \(u\)_q_\({}_{1}\)_, \(u\)_q_\({}_{1}\)_, \(exp\)_\({}_{1}\)_, \(trans\)_\({}_{1}\)_, \(inuse\)_\({}_{1}\)_, \(ctSchedule\)_\({}_{1}\)_, \(M\)\(>\) and \(UT_{2}^{M}\) = \(<\)_ptext\({}_{2}\)_, \(u\)_q_\({}_{2}\)_, \(u\)_q_\({}_{2}\)_, \(u\)_q_\({}_{2}\)_, \(exp\)_\({}_{2}\)_, \(inuse\)_\({}_{2}\)_, \(ctSchedule\)_\({}_{2}\)_, \(M\)\(>\) be units of thought registered by physician \(M\). A constraint on units of thought is defined in Equation (1), implying that each basic clinical idea is represented by a unique unit of thought. Considering that text variations do not change the meaning of a basic clinical idea, an ad hoc function _equal_ (defined in Equation (2)) is necessary to identify if two phrases represent the same clinical idea. The same clinical idea can be instanceal containing both structured information and random data, which implies that two different text strings can represent the same clinical idea.