Machine Learning vs. Rule-Based Methods for Document Classification of Electronic Health Records within Mental Health Care - A Systematic Literature Review

Machine Learning vs. Rule-Based Methods for Document Classification of Electronic Health Records within Mental Health Care - A Systematic Literature Review | 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 Machine Learning vs. Rule-Based Methods for Document Classification of Electronic Health Records within Mental Health Care - A Systematic Literature Review Emil Rijcken, Kalliopi Zervanou, Pablo Mosteiro, Floortje Scheepers, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2320804/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Document classification is a widely used approach for analysing mental healthcare texts. This systematic literature review focuses on document classification in healthcare notes obtained from electronic health records within mental health care. We observe that the last decade has been characterized by a shift from rule-based methods to machine-learning methods. However, while the shift towards machine-learning methods is evident, there is currently no systematic comparison of both methods for document classification in applications in mental healthcare. In this work, we perform a systematic literature review to assess how these methods compare in terms of performance, which are the specific applications and tasks, and how the approaches have developed throughout time. We find that for most of the last decade, rule-based methods have performed better than machine-learning methods. However, recent developments towards healthcare data availability in combination with self-learning neural networks and transformer-based large language models result in higher performance. Document Classification Natural Language Processing Electronic Health Records Mental Healthcare Machine Learning Rule-based methods Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2320804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281422924,"identity":"30a2bccf-3be2-4e87-9b58-6c5d22e26a23","order_by":0,"name":"Emil Rijcken","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PPQrCMBiA4S8E6pLateLfFVJ6gF6lRXAXFweHgGAWoWsL4hmcXA0UOnkGUbyAoIOgg1/VwSnpKJh3SGjIkzQANttPRg7VyIhwcJq8l5SeUP5Fdq8PqEUwJGReg3hAgVxh36GyTE6j1SNJwVVa0hIUaAfGjCyGRZhveJKLZqwlXHmK+hAzkjXmbRfJWjGuJZHCWz5E3t0lT7Ymwqu3nF/EKakr8BYwEL+gUACPq7cM2qwMw6wwEE/OyPE2iaNAlsGFTXvdVC6Cg47gfwEeCxCIrxVj5IZDv8ZGm81m+9OeLTI/3fEH7o8AAAAASUVORK5CYII=","orcid":"","institution":"Eindhoven University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Emil","middleName":"","lastName":"Rijcken","suffix":""},{"id":281422925,"identity":"948fe36c-c1ed-436e-815d-a72ef148fe12","order_by":1,"name":"Kalliopi Zervanou","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Kalliopi","middleName":"","lastName":"Zervanou","suffix":""},{"id":281422926,"identity":"a5fae14e-1a58-4200-bca2-290c1d7b55f8","order_by":2,"name":"Pablo Mosteiro","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Mosteiro","suffix":""},{"id":281422927,"identity":"d03d6210-9a35-46e2-bacc-d3cb8b3ab1da","order_by":3,"name":"Floortje Scheepers","email":"","orcid":"","institution":"University Medical Center Utrecht","correspondingAuthor":false,"prefix":"","firstName":"Floortje","middleName":"","lastName":"Scheepers","suffix":""},{"id":281422928,"identity":"4a0516e1-c951-4dd6-b09c-4521c9ee48bb","order_by":4,"name":"Marco Spruit","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Spruit","suffix":""},{"id":281422929,"identity":"203ce568-4dde-4e3d-b650-9494b635fa23","order_by":5,"name":"Uzay Kaymak","email":"","orcid":"","institution":"Eindhoven University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Uzay","middleName":"","lastName":"Kaymak","suffix":""}],"badges":[],"createdAt":"2022-11-28 11:59:27","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-2320804/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-2320804/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53203276,"identity":"84cdb1ff-0370-46fb-a303-c271abf3d294","added_by":"auto","created_at":"2024-03-21 20:44:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":535757,"visible":true,"origin":"","legend":"","description":"","filename":"LiteratureReviewclean.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2320804/v2_covered_2c19c93e-eb20-45a1-b40a-f3de60a364e6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMachine Learning vs. Rule-Based Methods for Document Classification of Electronic Health Records within Mental Health Care - 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