[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121317-en":3,"doc-seo-121317-105":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":20,"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},121317,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Prognostic Prediction Machine Learning Models for Elderly Pneumonia Patients - A Data-Driven Approach","Integrating diverse datasets with different predictors while keeping the same target variable strengthens both the performance and reliability of machine learning prognostic models. This thesis applies the idea to healthcare data, focusing on pneumonia in elderly patients in the Veneto region, by combining invasive bacterial diseases (IBD) notification forms with hospital discharge records (HDR) under deterministic feature-matching enabled by pseudonymization. Models based on SVMs, random forests, and artificial neural networks are trained separately and on the merged dataset, with the combined approach achieving the highest test ROC-AUC (0.861).","University of Padova  \nDepartment of Mathematics ”Tullio Levi-Civita”  \nMaster Thesis in Data Science  \nPrognostic Prediction Machine Learning Models for Elderly Pneumonia Patients  \nSupervisor Master Candidate  \nNicolò Navarin Claudio Palmeri  \nUniversity of Padova  \nCo-supervisor Vincenzo Baldo University of Padova  \nAcademic Year  \n2024-2025  \nii  \nThis thesis is dedicated to all the people who helped me and supported me during my academic career. Without you, Elisabetta, Corrado, Silvia, Luca, and many more, this would not have been possible.  \niv  \nAbstract  \nIntegrating di昀昀erent datasets with di昀昀erent predictors and the same target variable can enhance the performances and the reliability of machine learning models trained on them.  \nThis work applied this concept to healthcare data, speci昀椀cally pneumonia in elderly patients in the Veneto region, by combining the noti昀椀cation forms from the regional surveillance system for invasive bacterial diseases (IBD) dataset with the hospital discharge records dataset (HDR) .  \nDue to the datasets’ pseudonymization, deterministic techniques were used to exploit the fact that some features are shared to identify the matches between them. Predictive prognostic models were independently developed and evaluated for IBD, HDR, and the combined dataset.  \nThe models were implemented using machine learning techniques such as support vector machines, random decision forests, and arti昀椀cial neural networks. The best model trained on the combined dataset yielded higher performances than the models trained only on one of the two datasets.  \nMore precisely, the best model trained only on the IBD dataset, had an area under the ROC curve equal to 0.685 on the test set; the best model trained only on the HDR dataset had an area under the ROC curve equal to 0.793 on the test set; while the best model trained on the combined dataset had an area under the ROC curve equal to 0.861 on the test set.  \nAll of these models, especially the one trained on the combined dataset, could be used to identify higher-risk patients so that the hospital’s personnel could increase their likelihood of survival by surveilling them more.  \nvi  \nContents  \nAbstract v  \nList of figures ix  \nListing of acronyms xiii  \n1 Introduction 1  \n2 Related works 5  \n2.1 AI models in healthcare ............................ 5  \n2.1.1 Early applications of AI in healthcare ................. 5  \n2.1.2 Recent developments ......................... 7  \n2.2 Machine learning techniques used ....................... 9  \n2.2.1 Support vector machines ....................... 9  \n2.2.2 Random decision forests . . . . . . . . . . . . . . . . . . . . . . . 11  \n2.2.3 Arti昀椀cial Neural Networks ...................... 11  \n3 Datasets exploration and creation 13  \n3.1 Invasive bacterial diseases (IBD) dataset .................... 13  \n3.1.1 Year of diagnosis ........................... 14  \n3.1.2 Sex .................................. 15  \n3.1.3 Age of the patient ........................... 16  \n3.1.4 Associated invasive diseases ...................... 18  \n3.1.5 Sample material ............................ 19  \n3.1.6 Time of noti昀椀cation ......................... 21  \n3.1.7 Notifying hospital .......................... 22  \n3.1.8 Bacteria serotype ........................... 22  \n3.1.9 Vaccination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25  \n3.1.10 Antibiograms ............................. 27  \n3.2 Hospital discharge records (HDR) dataset .................. 27  \n3.2.1 Year of diagnosis ........................... 28  \n3.2.2 Hospital’s data ............................ 28  \n3.2.3 Age of the patient and date ofbirth .................. 30  \n3.2.4 Patient’s anagraphic data ....................... 31  \n3.2.5 Patient hospitalization ........................ 34  \n3.2.6 Patient diagnosis and procedures ................... 38  \n3.3 Combined dataset ............................... 42  \n4 Models 45  \n4.1 Baseline models ................","cbCaip7r9AMWwuR3","https://ap.wps.com/l/cbCaip7r9AMWwuR3","pdf",3221910,1,99,"English","en",105,"# Abstract\n# Introduction\n# Related works\n## AI models in healthcare\n## Machine learning techniques used\n# Datasets exploration and creation\n## Invasive bacterial diseases (IBD) dataset\n## Hospital discharge records (HDR) dataset\n## Combined dataset\n# Models\n## Baseline models\n## Artificial neural networks\n## Support vector machines\n## Random Forest\n# Experiments and results\n## IBD dataset\n## HDR dataset\n## Combined dataset\n## Best performing models\n# Conclusions\n# References\n# Acknowledgments","[{\"question\":\"What datasets are combined for the prognostic prediction task?\",\"answer\":\"The work combines the invasive bacterial diseases (IBD) notification forms from the regional surveillance system with the hospital discharge records (HDR) dataset for elderly pneumonia patients in Veneto.\"},{\"question\":\"How are the datasets matched despite pseudonymization?\",\"answer\":\"Because of dataset pseudonymization, deterministic techniques are used to exploit shared features and identify matches between records across datasets.\"},{\"question\":\"Which machine learning methods are used and what is the best performance?\",\"answer\":\"Models are built using support vector machines, random decision forests, and artificial neural networks. The best model trained on the combined dataset achieves a test ROC-AUC of 0.861, outperforming models trained on only one dataset.\"}]","Prognostic Prediction Machine Learning Models for Elderly Pneumonia Patients - A Data-Driven Approach | PDF",1785735044,249,{"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},"prognostic-prediction-machine-learning-models-for-elderly-pneumonia-patients-a-data-driven-approach","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prognostic-prediction-machine-learning-models-for-elderly-pneumonia-patients-a-data-driven-approach/121317/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What datasets are combined for the prognostic prediction task?","Question",{"text":75,"@type":76},"The work combines the invasive bacterial diseases (IBD) notification forms from the regional surveillance system with the hospital discharge records (HDR) dataset for elderly pneumonia patients in Veneto.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the datasets matched despite pseudonymization?",{"text":80,"@type":76},"Because of dataset pseudonymization, deterministic techniques are used to exploit shared features and identify matches between records across datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are used and what is the best performance?",{"text":84,"@type":76},"Models are built using support vector machines, random decision forests, and artificial neural networks. The best model trained on the combined dataset achieves a test ROC-AUC of 0.861, outperforming models trained on only one dataset.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]