Predicting drug consumption

Hospital wards are faced with day-to-day necessity to accurately forecast the consumption of drugs in order not to under stock nor to have too large inventories. This thesis explores the capabilities of some time series and some machine learning tools to predict future drug consumption based on ward status.

Candidate: Leonardo Bencinvega

Graduation: february 2018