## 3 Proposed TriagE-NLU System
#### Novelty Positioning  
This paper introduces a novel system, TriagE-NLU, that advances computational linguistics in the context of fast telemedicine triage. While NLP has seen extensive progress in areas such as clinical note parsing, intent classification, and dialogue systems, existing approaches fall short in several dimensions critical for high-stakes emergency interactions. Table 1 emphasizes the unique positioning of TriagE-NLU within the current landscape of clinical NLP and telemedicine research.  
We highlight the primary axes of originality below.  
* _Patient-Aware Semantic Understanding_: Unlike prior work focused on retrospective clinical texts, our system models spontaneous speech or dialogue between patients and clinicians. It fuses natural language with structured patient data (e.g., age, onset time, and vital signs), enabling patient-specific semantic parsing in time-sensitive emergency settings.
* _Grounding Semantic Representations in Interventions_: To our knowledge, TriagE-NLU is the first to bridge high-level semantic parsing with clinically actionable output. While prior systems have modeled symptom classification or medical Q&A, none have mapped parsed utterances to structured intervention suggestions in multilingual telemedicine contexts.
* _Multilingual and Low-Resource Triage Generalization_: Most existing systems are monolingual or assume idealized input. We propose a multilingual, low-resource capable system that performs fast semantic parsing across languages, using weak supervision and cross-lingual transfer. This enables deployment in under-resourced regions where emergency response support is critically needed.
* _Federated and Privacy-Preserving Learning_: TriagE-NLU integrates federated learning for continual improvement across decentralized clinical environments. This addresses key privacy and legal barriers to training on sensitive conversations while allowing regional adaptation to medical protocols, linguistic variance, and cultural expression of symptoms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature/Capability** & **Prior Work in Clinical NLP** & **TriagE-NLU** \\ \hline \multirow{2}{*}{Dialogue Understanding} & Mostly retrospective text [26; 27] & Supports multi-turn \\  & [26; 27] & emergency dialogue \\ \hline \multirow{2}{*}{Patient-Aware Semantic Parsing} & Typically text-only models & Integrates age, vitals, onset \\  & [9; 10] & metadata \\ \hline \multirow{2}{*}{Multilingual and Low-Resource Generalization} & \multirow{2}{*}{Monolingual focus [13; 21]} & Designed for multilingual \\  & & settings via transfer learning \\ \hline \multirow{2}{*}{Grounding in Medical Interventions} & Most focus on diagnosis or symptom extraction, not intervention mapping [28; 29] & Maps parsed intent to clinical \\  & & actions \\ \hline \multirow{2}{*}{Federated Learning for Privacy Adaptation} & \multirow{2}{*}{Centralized learning [30]} & Federated training with \\  & & privacy-preserving \\ \hline \multirow{2}{*}{Emergency Telemedicine Focus} & General clinical use or & Tailored to high-stakes, \\  & outpatient consults [31; 32] & urgent scenarios \\ \hline \multirow{2}{*}{Triage Benchmark with Interventions} & No available datasets with & \\  & multilingual, & \\ \multirow{2}{*}{} & intervention-labeled & Introduces TriageX dataset \\  & emergency conversations & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Novelty of TriagE-NLU compared to prior work in clinical NLP.  
* _Absence of Existing Benchmarks_: We introduce TriageX, a benchmark dataset of simulated multilingual emergency teleconsultation inputs. While dialogue datasets exist in general-purpose or open-domain contexts, there is no publicly available resource that combines medical urgency, multiple languages, and structured intervention annotation, establishing a new benchmark task for understanding semantic medical triage.