AI-Driven Decision Support in Emergency Medical Services for Real-Time Diagnosis and Hospital Coordination
Keywords:
Keywords: Emergency medical services, Decision support systems, Predictive analytics, Artificial Intelligence, Standard scaling, Disease prediction.Abstract
Emergency Medical Services (EMS) traditionally rely on manual vital-sign recording and verbalreporting, leading to delays, transcription errors, and inconsistent triage decisions. This project presentsan AI-driven decision-support system that automates pre-hospital diagnosis and hospital coordinationthrough two Tkinter-based applications: an ambulance-side client and a hospital-side server. The clientGUI enables batch upload of patient vital-sign CSV files, serializes each record, and transmits it overTCP to the server. The server GUI logs incoming data, applies a StandardScaler for featurenormalization, and performs real-time inference using four machine-learning classifiers
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