## 8 Conclusions and Future Work  
This paper introduced TriagE-NLU, a novel, multilingual clinical NLP system designed for interpretation and intervention support in telemedicine contexts. Unlike prior approaches that specialize in either semantic parsing or classification, TriagE-NLU unifies both capabilities under a federated, low-resource-optimized architecture. Evaluated across multilingual emergency scenarios, the model demonstrated state-of-the-art performance, achieving 0.91 F1 in semantic parsing, 0.89 F1 in intervention classification, 0.92 overall accuracy, and a BLEU score of 0.87, substantially outperforming established baselines like mT5-Base, mBART + MLP, and XLM-RoBERTa + BiLSTM.  
Our findings highlight TriagE-NLU's ability to generalize across languages and conditions, offering actionable medical understanding even in low-connectivity, multilingual rural environments. The architecture's integration of structured symptom reasoning and decision grounding, combined with federated learning for privacy-preserving adaptation, marks a significant contribution to clinical NLP and digital health infrastructure. To our knowledge, this is the first system that jointly addresses multilingual clinical semantic parsing and intervention recommendation in a federated triage setting.  
These results affirm that TriagE-NLU is not only a technical advancement but also a possible future deployable solution with high clinical utility in time-sensitive emergency workflows.  
In future work, we plan to pursue several concrete directions to strengthen TriagE-NLU's capabilities further. First, we will extend the system beyond initial triage to support broader diagnostic reasoning, including differential diagnosis and follow-up care sugges  
Figure 9: Heatmap showing performance impact of ablation studies on TriagE-NLU across key evaluation metrics, highlighting the contribution of each architectural component.  
tions. Second, we aim to incorporate wearable sensor data (e.g., vitals, motion tracking, fall detection) into the NLU pipeline, enabling multimodal triage interpretation that combines natural language with physiological signals. Third, we are working to expand the TriageX dataset with more clinically diverse cases, additional low-resource languages, and transcripts to improve robustness and coverage.  
In parallel, we intend to initiate collaborations with healthcare providers, EMS agencies, and policy stakeholders to pilot the integration of Triage-NLU into emergency response workflows. Such partnerships will be critical for validating system impact in practice, refining usability under operational constraints, and aligning the model with regulatory and clinical governance standards.  
In conclusion, we consider Triage-NLU a promising step toward enhancing digitally supported emergency care, with the potential to improve access to reliable triage assistance across diverse linguistic, geographic, and resource-constrained settings.  
Conceptualization, B.-M.B. and I.S.; methodology, B.-M.B.; software, B.-M.B.; validation, B.-M.B., I.S. and A.-C.-P.-N.; investigation, B.-M.B.; data curation, B.-M.B. writing--original draft preparation, B.-M.B.; visualization, B.-M.B.; supervision, I.S. and A.-C.-P.-N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
TriageX dataset and further inquiries are available on request from the authors.  
The authors declare no conflicts of interest.