# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 5 The Emergency Strategy Case Study: Needs Analysis and Formalization
### Simplified Example  
We illustrate two distinct scenarios involving patients who exhibit the same symptoms (s1, s5, s9, and s10) but differ in their indicators, such as age and medical history. These differences lead to unique results in the generated knowledge graphs. **Scenario 1: A 75-year-old man** The first scenario involves a 75-year-old man with no prior health conditions. Despite having the same symptoms as the second patient, his age and lack of medical history significantly impact the resulting knowledge graph. The patient's age serves as a key personal indicator, influencing the identification of possible diseases. After processing this information, the resulting graph, referred to as PKG1, is generated. PKG1 reflects the relationships between the symptoms and potential conditions most relevant to this patient's age group. Figure 4A shows this graph, providing a visual representation of how the patient's characteristics, like age, affect the input graph for the analysis. **Scenario 2: A 9-month-old baby** The second scenario involves a 9-month-old baby, also with no prior health conditions. While this patient shares the same symptoms (s1, s5, s9, and s10) as the 75-year-old man, the baby's age significantly alters the resulting knowledge graph. In this case, age is a critical factor that redefines the potential conditions and diseases linked to the symptoms. The resulting graph, PKG2, is generated after inputting the baby's  
Figure 4: (**A**) PKG1—the graph for the 75-year-old man, (**B**) PKG2—the graph for the 9-month-old baby. indicators. PKG2 highlights the diseases and medical considerations that are more relevant to infants, reflecting the distinct medical risks associated with early childhood. Figure 4B presents this graph, showing how the patient's age impacts the analysis and identifying relevant health concerns. In both scenarios, despite the identical symptoms, the personal indicators--age and health history--lead to different interpretations and analyses, as evidenced by the differing graphs (PKG1 and PKG2). These graphs demonstrate the importance of considering individual patient characteristics when diagnosing and identifying potential conditions. It is important to note that these two scenarios produce different graphs, meaning the algorithms process different inputs and generate distinct hypotheses for each patient. This variation in the inputs directly affects the communities and risks that are evaluated, ultimately influencing the hypotheses generated by the algorithms. In the first scenario, the 75-year-old man with no prior health conditions, the graph (PKG1) only includes the communities C1 and C3. The algorithm analyzes these two communities and calculates the associated risks. The risk levels of each community--denoted as Risk(C1) and Risk(C3)--are compared. Since **Risk(C3) \(\geq\) Risk(C1)**, the algorithm determines that the disease most likely associated with community C3 should be prioritized for exclusion. In this case, disease **d5** is ruled out first. In the second scenario, involving the 9-month-old baby, the graph (PKG2) includes a broader range of communities, as the personal indicator--age--results in a wider set of communities being considered. The algorithm considers all relevant communities: C1, C2, and C3. After evaluating the risks associated with each community, the algorithm compares them and finds that **Risk(C1) \(\geq\) Risk(C2) = Risk(C3)**. Since **Risk(C1) \(\geq\) Risk(C2)**, the disease associated with community C1 is ruled out first. In this case, disease **d2** is the first to be excluded. **Key differences in the scenarios:**  
**Graph construction:** The primary difference between the two scenarios is the composition of the graph. In the first scenario, only two communities (C1 and C3) are involved, while in the second scenario, all communities are considered. **Risk calculation**: The risk values of each community differ in the two scenarios due to the varying personal indicators (age, health history).