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Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach
Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Arkitektur och vatten.ORCID-id: 0000-0003-3438-1182
School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong.
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2022 (Engelska)Ingår i: Information, E-ISSN 2078-2489, Vol. 13, nr 9, artikel-id 410Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods.

Ort, förlag, år, upplaga, sidor
MDPI , 2022. Vol. 13, nr 9, artikel-id 410
Nyckelord [en]
emergency department, machine learning, temperature, older adult, Hong Kong
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Sannolikhetsteori och statistik
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URN: urn:nbn:se:ltu:diva-93746DOI: 10.3390/info13090410ISI: 000856391800001Scopus ID: 2-s2.0-85138719461OAI: oai:DiVA.org:ltu-93746DiVA, id: diva2:1707023
Anmärkning

Validerad;2022;Nivå 2;2022-10-28 (sofila);

Funder: University of Hong Kong (201811159222)

Tillgänglig från: 2022-10-28 Skapad: 2022-10-28 Senast uppdaterad: 2023-09-05Bibliografiskt granskad

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Lau, Kevin Ka-Lun

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