Case Study Archive
NWAS – Ambulance data exploration
Data exploration of ambulance service
The aim of this proof-of-concept project was to develop a machine learning model that could predict the triage outcome of emergency calls based on the information provided by the caller. The model was trained on a large dataset of emergency call data and triage outcomes to identify patterns and relationships between the information provided and the resulting triage classification.
Two different AI approaches were involved in the developed models, including using a gradient-boosted decision trees model for the numerical and categorical type of data, and a NLP model to handle the free-text data.