[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124325-en":3,"doc-seo-124325-105":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},124325,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning for Adverse Event Analysis in Healthcare - Identifying Opportunities and Conducting a Classification Study - Master’s thesis","Adverse events, defined as unintended harm to patients, are a key indicator of hospital patient safety and include both preventable and unavoidable cases. The thesis investigates how machine learning can strengthen adverse event reporting and analysis at St. Olav’s Hospital. Using 46,087 reports from 2015–2022, it applies machine learning to textual content, guided by clinician and expert interviews, and focuses on feasibility of automatic classification. Two models, Naive Bayes and SVM, are tuned and evaluated.","Master’s thesis  \nNT NU  \nNorwegian Un iversity of Science and Technology  \nFaculty of Information Techno logy and Electrical Engineering Department of Computer Science  \nRagnhild Kleiven and Solveig Hergot Langås  \nMachine Learning for Adverse Event Analysis in Healthcare  \nIdentifying Opportunities and Conducting a Classification Study  \nMaster’s thesis in Computer Science Supervisor: Øystein Nytrø  \nCo-supervisor: Melissa Yan June 2023  \nRagnhild Kleiven and Solveig Hergot Langås  \nMachine Learning for Adverse Event Analysis in Healthcare  \nIdentifying Opportunities and Conducting a Classification Study  \nMaster’s thesis in Computer Science Supervisor: Øystein Nytrø  \nCo-supervisor: Melissa Yan June 2023  \nNorwegian University of Science and Technology  \nFaculty of Information Technology and Electrical Engineering Department of Computer Science  \ni  \nAbstract  \nAdverse events, which refer to unintended harm caused to patients, serve as a crucial indicator of patient safety within hospitals. While some are unavoidable, research has revealed that many adverse events are preventable. Thus it is crucial for hospitals to have a systematic approach to address adverse events, enabling analysis and insights for preventive measures to enhance patient safety.  \nThis thesis aims to research how applications of machine learning can aid in improving the adverse event reporting and analysis process at St. Olav’s Hospital. With access to a dataset of 46,087 adverse event reports spanning from 2015 to 2022, the thesis explores how the textual data in the reports can be utilized with machine learning to enhance the current process.  \nThe thesis has two main objectives: firstly, identifying potential areas within the adverse event reporting and analysis process where machine learning can contribute to improvement, and secondly, selecting a specific area to conduct a machine learning study in order to validate its feasibility and reliability. To gain insights into the current process, interviews were conducted with clinicians and experts. These interviews provided valuable insights into the existing process and helped identify areas that could benefit from enhancements.  \nOne area identified from the interviews where the automatic classification of adverse events according to the National guidelines for classification of patientrelated adverse events (NOKUP) . This application can potentially improve the current process by addressing issues such as misclassification and inconsistent categorization that hinder accurate analysis and identification of preventive measures. With these benefits in mind and confirming the interest of the clinicians, this application was selected for the machine learning study.  \nThe machine learning study aimed to verify the feasibility of the classification of adverse events into predefined categories. It explored the potential of two distinct classification techniques, N¨aive Bayes (NB) and Support Vector Machine (SVM) . Several experiments were conducted to optimize the performance of both classifiers. The resulting macro F1-score for NB was 0.7182 and 0.7165 for SVM. Although these results could be considered reliable in other domains, further improvement is needed before the models could be implemented in a healthcare context. However, they demonstrate the potential of automatic classification of adverse events.  \nThus, this thesis has provided a foundation for the automatic classification of adverse events, demonstrating its significance and potential. Moreover, in collaboration with clinicians, two additional machine learning applications have been identified, providing valuable insights for future research directions.  \nii  \nSammendrag  \nUønskede hendelser er en nøkkelindikator for pasientsikkerhet p˚a sykehus. Slike hendelser innebærer utilsiktede pasientskader, og selv om noen av disse er uunng˚aelige, viser forskning at mange uønskede hendelser kan forhindres. For˚a bedre pasientsikkerheten er det viktig ","cbCaiabWdofa46jk","https://ap.wps.com/l/cbCaiabWdofa46jk","pdf",12649345,1,138,"English","en",105,"# Abstract\n## Background and objective\n## Dataset and interview insights\n## NOKUP-based classification application\n## Classification methods and evaluation results\n## Conclusions and future directions","[{\"question\":\"Why is adverse event reporting important in hospitals?\",\"answer\":\"Adverse events are unintended harm to patients and serve as a crucial indicator of patient safety. Many adverse events can be prevented, which motivates systematic analysis for preventive measures.\"},{\"question\":\"What is the main aim of the thesis at St. Olav’s Hospital?\",\"answer\":\"The thesis evaluates how machine learning can improve adverse event reporting and analysis by leveraging textual information in a large set of reports. It also identifies improvement opportunities and validates feasibility through a targeted study.\"},{\"question\":\"Which classification techniques were tested, and what were the results?\",\"answer\":\"The thesis tests Naive Bayes (NB) and Support Vector Machine (SVM) for classifying adverse events into predefined NOKUP categories. The reported macro F1-scores are 0.7182 for NB and 0.7165 for SVM, indicating potential but requiring further improvement for healthcare deployment.\"}]","Machine Learning for Adverse Event Analysis in Healthcare - Identifying Opportunities and Conducting a Classification Study - Master’s thesis | PDF",1785821615,348,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-adverse-event-analysis-in-healthcare-identifying-opportunities-and-conducting-a-classification-study-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-adverse-event-analysis-in-healthcare-identifying-opportunities-and-conducting-a-classification-study-masters-thesis/124325/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is adverse event reporting important in hospitals?","Question",{"text":75,"@type":76},"Adverse events are unintended harm to patients and serve as a crucial indicator of patient safety. Many adverse events can be prevented, which motivates systematic analysis for preventive measures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main aim of the thesis at St. Olav’s Hospital?",{"text":80,"@type":76},"The thesis evaluates how machine learning can improve adverse event reporting and analysis by leveraging textual information in a large set of reports. It also identifies improvement opportunities and validates feasibility through a targeted study.",{"name":82,"@type":73,"acceptedAnswer":83},"Which classification techniques were tested, and what were the results?",{"text":84,"@type":76},"The thesis tests Naive Bayes (NB) and Support Vector Machine (SVM) for classifying adverse events into predefined NOKUP categories. The reported macro F1-scores are 0.7182 for NB and 0.7165 for SVM, indicating potential but requiring further improvement for healthcare deployment.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]