[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118865-en":3,"doc-seo-118865-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118865,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Predicting Outcomes of Smoking Cessation Interventions in Novel Scenarios Using Ontology-Informed, Interpretable Machine Learning","Ontology-informed, interpretable machine learning is developed to predict smoking cessation outcomes when intervention scenarios differ from prior evidence. Systematic reviews typically estimate relative average effects from existing trials, but policymakers and practitioners need outcome predictions for novel intervention contexts, populations, or settings. Using 405 randomized trial reports annotated with the Behaviour Change Intervention Ontology, the approach learns interpretable rules to forecast cessation rates. Results evaluate moderate predictive accuracy via cross-validation and compare against competing machine learning methods.","RESEARCH ARTICLE  \nPredicting outcomes of smoking cessation interventions in novel scenarios using ontology-informed, interpretable machine learning [version 1; peer review: awaiting peer review]  \nJanna Hastings 1,2, Martin Glauer3, Robert West 4, James Thomas 5,  \nAlison J. Wright6, Susan Michie 7  \n1Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Zürich  \n2School of Medicine, University of St Gallen, St. Gallen  \n3Institute for Intelligent Interacting Systems, Otto von Guericke Universitat Magdeburg, Magdeburg, Saxony-Anhalt  \n4 Research Department of Behavioural Science and Health, University College London, London, England  \n5 EPPI-Centre, Social Research Institute, University College London, London, England  \n6Institute of Pharmaceutical Science, King's College London, London, England  \n7Centre for Behaviour Change, University College London, London, England  \nv1  \nFirst published: 07 Nov 2023, 8:503  \n[https://doi.org/10.12688/wellcomeopenres.20012.1](https://doi.org/10.12688/wellcomeopenres.20012.1)  \n[Latest published:](Latest published: 07 Nov 2023)[ 07 Nov 2023](Latest published: 07 Nov 2023), 8:503  \n[https://doi.org/10.12688/wellcomeopenres.20012.1](https://doi.org/10.12688/wellcomeopenres.20012.1)  \nAbstract  \nOpen Peer Review  \nApproval Status AWAITING PEER REVIEW  \nAny reports and responses or comments on the  \narticle can be found at the end of the article.  \nBackground  \nSystematic reviews of effectiveness estimate the relative average effects of interventions and comparators in a set of existing studies e.g., using rate ratios. However, policymakers, planners and practitioners require predictions about outcomes in novel scenarios where aspects of the interventions, populations or settings may differ. This study aimed to develop and evaluate an ontology-informed, interpretable machine learning algorithm to predict smoking cessation outcomes using detailed information about interventions, their contexts and evaluation study methods. This is the second of two linked papers on the use of machine learning in the Human Behaviour-Change Project.  \nMethods  \nThe study used a corpus of 405 reports of randomised trials of smoking cessation interventions from the Cochrane Library database. These were annotated using the Behaviour Change Intervention  \nOntology to classify, for each of 971 study arms, 82 features representing details of intervention content and delivery, population, setting, outcome, and study methodology. The annotated data was used to train a novel machine learning algorithm based on a set of interpretable rules organised according to the ontology. The algorithm was evaluated for predictive accuracy by performance in five-fold 80:20 cross-validation, and compared with other approaches.  \nResults  \nThe machine learning algorithm produced a mean absolute error in prediction percentage cessation rates of 9.15% in cross-validation, outperforming other approaches including an uninterpretable ‘blackbox’ deep neural network (9.42%), a linear regression model (10.55%) and a decision tree-based approach (9.53%) . The rules generated by the algorithm were synthesised into a consensus rule set to create a publicly available predictive tool to provide outcome predictions and explanations in the form of rules expressed in terms of predictive features and their combinations.  \nConclusions  \nAn ontologically-informed, interpretable machine learning algorithm, using information about intervention scenarios from reports of smoking cessation trials, can predict outcomes in new smoking cessation intervention scenarios with moderate accuracy.  \nKeywords  \nbehaviour change interventions, Artificial Intelligence, machine learning, natural language processing, prediction systems, information extractions, ontologies, evidence synthesis  \nThis article is included in the Human BehaviourChange Project collection.  \nCorresponding author: Janna Hastings ([janna.hastings@uzh.ch](jann","cbCaisMI4nX51aly","https://ap.wps.com/l/cbCaisMI4nX51aly","pdf",1130468,1,17,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the need to predict smoking cessation outcomes in novel scenarios where intervention components, populations, or settings differ from existing trials.\"},{\"question\":\"How is the machine learning model built?\",\"answer\":\"A corpus of randomized trial reports is annotated using the Behaviour Change Intervention Ontology to extract features, then an interpretable rule-based machine learning algorithm is trained to predict cessation outcomes.\"},{\"question\":\"How accurate are the predictions and how do they compare to other methods?\",\"answer\":\"The model achieves a mean absolute error of 9.15% in cross-validation, outperforming an uninterpretable deep neural network, a linear regression model, and a decision tree-based approach.\"}]","Predicting Outcomes of Smoking Cessation Interventions in Novel Scenarios Using Ontology-Informed, Interpretable Machine Learning | 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problem does the study address?","Question",{"text":75,"@type":76},"The study addresses the need to predict smoking cessation outcomes in novel scenarios where intervention components, populations, or settings differ from existing trials.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the machine learning model built?",{"text":80,"@type":76},"A corpus of randomized trial reports is annotated using the Behaviour Change Intervention Ontology to extract features, then an interpretable rule-based machine learning algorithm is trained to predict cessation outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the predictions and how do they compare to other methods?",{"text":84,"@type":76},"The model achieves a mean absolute error of 9.15% in cross-validation, outperforming an uninterpretable deep neural network, a linear regression model, and a decision tree-based 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