Internship report: K-Means clustering EWS data
July 1st, 2020During an internship at Arinti, Senne (Erasmushogeschool Brussel) worked with an anonymised dataset of over 170,000 Early Warning Score (EWS) measurements. The EWS score aggregates vital parameters like heart rate, blood pressure, and body temperature to indicate a patient's overall condition.
From exploration to clustering
After data exploration in Python — visualising distributions, filtering healthy vs unhealthy data, and identifying boundary zones — the project moved to K-Means clustering. The algorithm took patients with 100+ measurements, extracted their 87 most recent EWS values, and assigned cluster labels based on health trajectory trends.
Dashboard and results
An Angular web dashboard displayed monitoring data per patient, with colour-coded values for danger zones. The clustering results showed each patient's category with an explanation of what it means for their health outlook. This can help doctors and nursing staff assess patient condition and predict future trajectory. — Senne, Erasmushogeschool Brussel
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