Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms

Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms Bruno Matos Porto, Flavio S. Fogliatto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3891200/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 17 You are reading this latest preprint version Abstract Background Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of patient arrivals in EDs can serve as a management baseline for better allocation of staff and medical resources. In this article, we investigate the use of calendar and meteorological predictors, as well as feature engineered variables, to forecast daily patient arrivals using datasets from eleven different EDs across 3 countries. Methods Six machine learning algorithms were tested, considering forecasting horizons of 7 and 45 days ahead. Tuning of hyperparameters was performed using a grid-search with cross-validation. Algorithms' performance was evaluated using 5-fold cross-validation and four performance metrics. Results The eXtreme Gradient Boosting (XGBoost) achieved better performance considering the two prediction horizons compared to other models, also outperforming results reported in past studies on ED arrival prediction. This is also the first study to utilize Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF) and Neural Network Autoregression (NNAR) for predicting patient arrivals at EDs. Conclusion The Random Forest (RF) variable selection and grid-search methods improved the accuracy of the algorithms tested. Our study innovates by using feature engineering to predict patient arrivals in EDs. Emergency department Feature engineering Machine learning algorithms Patient visits forecast Time series forecasting Full Text Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 18 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviews received at journal 31 May, 2024 Reviews received at journal 31 May, 2024 Reviews received at journal 27 May, 2024 Reviewers agreed at journal 23 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers agreed at journal 10 May, 2024 Reviews received at journal 02 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers invited by journal 30 Apr, 2024 Editor invited by journal 31 Jan, 2024 Editor assigned by journal 31 Jan, 2024 Submission checks completed at journal 31 Jan, 2024 First submitted to journal 23 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3891200","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270264094,"identity":"be7704da-7963-4c54-a8bf-a2b0b633fc37","order_by":0,"name":"Bruno Matos Porto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACAyQ24wMgwcNHhBbGBjCLjYEZpJ+HjRQtbBIQmgAwFzv8/MGHP3fkzOc3H6v8mmMnA7Tt4aMbeLRYzk4zbJzZ9sxY5hhb2m3ZbclAh7EZG+fgc9jtBMNm3obDiTPYeMxuS25jBmrhYZPGryX9YzPPH4iWYslt9cRoyTFs5mGDaGH8uO0wYS2Ws3MKZ4L8IsGWlizNuO04DxszAb+YS6dv+AAKMQnmwwc//txWbc/P3vzwMT4tUHAATDLzgEnCyhFaGH8Qp3oUjIJRMApGGAAAAfJGXKmJILAAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Rio Grande do Sul","correspondingAuthor":true,"prefix":"","firstName":"Bruno","middleName":"Matos","lastName":"Porto","suffix":""},{"id":270264095,"identity":"edbe5544-b2b1-4d30-95fb-b64a697fadfa","order_by":1,"name":"Flavio S. 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Accurate predictions of patient arrivals in EDs can serve as a management baseline for better allocation of staff and medical resources. In this article, we investigate the use of calendar and meteorological predictors, as well as feature engineered variables, to forecast daily patient arrivals using datasets from eleven different EDs across 3 countries.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSix machine learning algorithms were tested, considering forecasting horizons of 7 and 45 days ahead. Tuning of hyperparameters was performed using a grid-search with cross-validation. Algorithms' performance was evaluated using 5-fold cross-validation and four performance metrics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe eXtreme Gradient Boosting (XGBoost) achieved better performance considering the two prediction horizons compared to other models, also outperforming results reported in past studies on ED arrival prediction. 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Our study innovates by using feature engineering to predict patient arrivals in EDs.\u003c/p\u003e","manuscriptTitle":"Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 07:04:45","doi":"10.21203/rs.3.rs-3891200/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-18T12:19:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T19:44:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-31T19:44:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-31T11:16:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-27T22:24:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60935034768038210169719694667068406880","date":"2024-05-23T07:00:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189491440224198116509962446419491565391","date":"2024-05-21T16:08:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153097678818423861440734656647938377212","date":"2024-05-21T07:37:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260455869166954035991954697000700722220","date":"2024-05-10T22:21:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-02T13:37:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176922526169385044705895768472213070784","date":"2024-05-02T12:37:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29666374492054507597246286824707199869","date":"2024-05-02T12:33:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-30T11:22:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-31T10:47:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-31T10:43:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-31T10:43:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2024-01-23T13:28:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8253f789-eb38-4b4b-bfd0-26b5942ee175","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:00:31+00:00","versionOfRecord":{"articleIdentity":"rs-3891200","link":"https://doi.org/10.1186/s12911-024-02788-6","journal":{"identity":"bmc-medical-informatics-and-decision-making","isVorOnly":false,"title":"BMC Medical Informatics and Decision Making"},"publishedOn":"2024-12-18 15:57:12","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-02-02 07:04:45","video":"","vorDoi":"10.1186/s12911-024-02788-6","vorDoiUrl":"https://doi.org/10.1186/s12911-024-02788-6","workflowStages":[]},"version":"v1","identity":"rs-3891200","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3891200","identity":"rs-3891200","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}