Forecasting patient flows with pandemic induced concept drift using explainable machine learning

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dc.citation.issue1
dc.citation.volume12
dc.contributor.authorSusnjak T
dc.contributor.authorMaddigan P
dc.coverage.spatialGermany
dc.date.accessioned2024-10-08T22:05:59Z
dc.date.available2024-10-08T22:05:59Z
dc.date.issued2023-04-21
dc.description.abstractAccurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns. This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions. This research also uniquely contributes to the body of work in this domain by employing tools from the eXplainable AI field to investigate more deeply the internal mechanics of the models than has previously been done. The Voting ensemble-based method combining machine learning and statistical techniques was the most reliable in our experiments. Our study showed that the prevailing COVID-19 Alert Level feature together with Google search terms and pedestrian traffic were effective at producing generalisable forecasts. The implications of this study are that proxy variables can effectively augment standard autoregressive features to ensure accurate forecasting of patient flows. The experiments showed that the proposed features are potentially effective model inputs for preserving forecast accuracies in the event of future pandemic outbreaks.
dc.description.confidentialfalse
dc.format.pagination11-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37122585
dc.identifier.citationSusnjak T, Maddigan P. (2023). Forecasting patient flows with pandemic induced concept drift using explainable machine learning.. EPJ Data Sci. 12. 1. (pp. 11-).
dc.identifier.doi10.1140/epjds/s13688-023-00387-5
dc.identifier.eissn2193-1127
dc.identifier.elements-typejournal-article
dc.identifier.issn2193-1127
dc.identifier.number11
dc.identifier.pii387
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71637
dc.languageeng
dc.publisherBioMed Central Ltd
dc.publisher.urihttps://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-023-00387-5
dc.relation.isPartOfEPJ Data Sci
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectConcept drift
dc.subjectExplainable AI
dc.subjectForecasting
dc.subjectInterpretable machine learning
dc.subjectMachine learning
dc.subjectPatient flows
dc.titleForecasting patient flows with pandemic induced concept drift using explainable machine learning
dc.typeJournal article
pubs.elements-id461298
pubs.organisational-groupOther
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