## 7 Results and Performance Analysis  
### Use Case: Rural Telemedicine Stroke Suspect Case  
In this subsection it is described how the TriagE-NLU would analyze and respond to a stroke suspect scenario in a rural telemedicine setting. It highlights how TriagE-NLU interprets speech, enriches it with patient metadata, parses relevant clinical indicators, and outputs a structured summary along with a recommended intervention.  
A visual representation of the use case is shown in Figure 6.  
A 62-year-old woman with a history of hypertension and prior stroke is reported by her daughter to be \"talking strangely,\" feeling dizzy, and unable to move her arm. This acute symptom onset occurred approximately five minutes ago. The clinician is not  
Figure 6: Use-case scenario for TriagE-NLU.  
physically present onsite, and the telemedicine platform relies on TriagE-NLU to process this input.  
The patient's metadata (age: 62, pre-existing conditions: hypertension, stroke) and free-text dialogue are combined into a unified multilingual input string that includes the patient's spoken description and structured clinical context. This input is preprocessed by appending patient age and comorbidities using delimiters, such as [SEP], to form a model-readable string. This format enables TriagE-NLU to jointly encode unstructured text and relevant background information, eliminating the need for separate input channels.  
Upon receiving this input, the TriagE-NLU model simultaneously performs semantic parsing and classification. The parsing head identifies symptoms (e.g., slurred speech, unilateral arm weakness), onset time (\"sudden\"), and urgency (\"high\"), structuring them into a EUREKA frame. In parallel, the classification head maps the structured understanding to an appropriate medical action, in this case, \"activate stroke protocol\".  
The output returned to the clinician consists of both the structured semantic representation (\"EUREKA FRAME OUTPUT: <extra_id_0>: a stroke\") and the recommended intervention (\"RECOMMENDED INTERVENTION: activate stroke protocol\"). This enables the clinician to escalate care immediately without having to interpret the patient's message manually. The entire process, from dialogue input to clinical action, showcases how TriagE-NLU can support timely, life-saving decisions in underserved areas.  
Upon receiving the system's recommendation, the remote clinician immediately initiates the stroke protocol. This includes instructing the onsite staff to prepare for emergency transport and notifying the nearest medical center in advance. Because TriagE-NLU has already extracted and structured the critical indicators of stroke, such as symptom onset within 5 min, speech impairment, and unilateral limb weakness, the clinician can make a rapid, evidence-backed decision without needing to parse the patient's report manually. This significantly reduces the time to intervention, which is crucial in stroke care where every minute counts.  
TriagE-NLU's Python 3.11.9 code acts as an interpreter between unstructured dialogue and life-saving clinical decision support. It removes ambiguity from natural language and converts it into standardized medical actions.