Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data.
Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with its detrimental effects. Recent findings in our ED indicate tha...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
1/15/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182212689&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182212689 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 1/15/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182212689 182212689 182212689 10.1007/s10916-024-02137-0 182212689 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data. aug: au: Nevanlinna, Jalmari Eidstø, Anna Ylä-Mattila, Jari Koivistoinen, Teemu Oksala, Niku Kanniainen, Juho Palomäki, Ari Roine, Antti affil: https://ror.org/033003e23 Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland sug: subj: Hospital Mortality Risk Factors Emergency Service Bed Occupancy Boosting Machine Learning Algorithms Prediction Models Funding Source Finland Human Hospitals, Urban Academic Medical Centers Retrospective Design Weather ROC Curve Confidence Intervals Descriptive Statistics Surgical Patients Medical Care Crowding ab: Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with its detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective time series data such as weather, availability of hospital beds, calendar variables and occupancy statistics from a large Nordic ED with a LightGBM model. We predict mortality associated crowding for the whole ED and individually for its different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using time series data is feasible. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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