Using Bayesian Networks to forecast ancillary service volume in hospitals

A Python example using diagnostic input variables

Since I’ve been working with healthcare data (almost 10 years now), forecasting future patient volume has been a tough nut to crack. There are so many dependencies to consider — patient requests and severity, administrative needs, exam room constraints, a provider just called out sick, a bad snow storm. Plus, unanticipated scenarios can have cascading impacts on scheduling and resource allocation that contradict even the best Excel …

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