[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127540-en":3,"doc-seo-127540-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127540,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","ORIENTATE: automated machine learning classifiers for oral health prediction and research","Application of data-driven methods is increasingly valuable in healthcare, yet adoption is hindered by a shortage of clinicians with the technical skills needed to build and interpret machine-learning outputs. ORIENTATE is presented as software enabling clinicians to select features and a target variable, automatically generate and cross-validate multiple classification models, and identify the best-performing one. It performs systematic feature selection and produces comprehensive graphical reports for global interpretation and prediction of new samples, supporting statistical inference.","Gomez‑Rios et al. BMCOral Health (2023) 23:408 [https://doi.org/10.1](https://doi.org/10.1) 186/s12903‑023‑03112‑w  \nBMC Oral Health  \n RESEARCH Open Access  \nORIENTATE: automated machine learning   classifiers for oral health prediction and research  \nInmaculada Gomez‑Rios 1, Esteban Egea‑Lopez2* and Antonio José Ortiz Ruiz1  \nAbstract  \nBackground The application of data‑driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is prevent‑ ing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific techni‑ cal skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross‑validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/ or complement classical statistical studies.  \nResults Its application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided.  \nConclusions ORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation.  \nKeywords Machine learning, Classification, Special health care needs, Deep sedation, Predictive dentistry, Second sedation risk  \n*Correspondence: Esteban Egea‑Lopez esteban.egea@upct.es  \n1 Department of Dermatology, Stomatology, Radiology and Physical Medicine, Universidad de Murcia, Murcia, Spain  \n2 Dept. Information Technologies and Communications, Universidad Politecnica de Cartagena (UPCT), Cartagena, Spain  \nBackground  \nThe application of data-driven methods is expected to play an increasingly important role in healthcare [1, 2], as they can be a particularly effective tool for diagnosis (disease presence) or prognosis (risk of future outcome), among other tasks. To mention a few representative examples: they have been used for the  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the art","cbCaimPl9Zm3z1Dl","https://ap.wps.com/l/cbCaimPl9Zm3z1Dl","pdf",2953903,2,1,19,"English","en",105,"# Abstract\n## Background\n## Results\n## Conclusions\n# Keywords","[{\"question\":\"What problem does ORIENTATE address in healthcare machine learning?\",\"answer\":\"ORIENTATE addresses the skills gap that limits broader use of data-driven machine-learning methods in healthcare, particularly the ability to develop classification models and interpret their results.\"},{\"question\":\"How does ORIENTATE build and evaluate classification models?\",\"answer\":\"It lets users select features and the target variable, then automatically generates multiple classification models, cross-validates them, and selects and evaluates the best model.\"},{\"question\":\"What does the case study show about predicting second sedation risk?\",\"answer\":\"In a dataset of children receiving deep sedation, ORIENTATE’s feature selection achieved strong performance (F1=0.83, ROC AUC=0.92) for predicting the need for a second sedation, and tooth-related predictors were ranked by relevance.\"}]","ORIENTATE: automated machine learning classifiers for oral health prediction and research | 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problem does ORIENTATE address in healthcare machine learning?","Question",{"text":76,"@type":77},"ORIENTATE addresses the skills gap that limits broader use of data-driven machine-learning methods in healthcare, particularly the ability to develop classification models and interpret their results.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does ORIENTATE build and evaluate classification models?",{"text":81,"@type":77},"It lets users select features and the target variable, then automatically generates multiple classification models, cross-validates them, and selects and evaluates the best model.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the case study show about predicting second sedation risk?",{"text":85,"@type":77},"In a dataset of children receiving deep sedation, ORIENTATE’s feature selection achieved strong performance (F1=0.83, ROC AUC=0.92) for predicting the need for a second sedation, and tooth-related predictors were ranked by 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