[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127882-en":3,"doc-seo-127882-105":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},127882,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning - Research study","Smartphone apps for cancer patients receiving systemic treatment can enable early detection of symptoms and therapy side effects, with machine learning supporting timely therapy adaptation and fewer adverse outcomes and unplanned admissions. This study builds an Early Warning System that predicts when supportive interventions are needed to prevent unplanned visits by analyzing dynamically collected standardized ePRO data together with vital parameters, medication information, and free text within each patient’s clinical journey. A whitebox rule-learning hybrid model is combined with oncological expert review to address imbalanced adverse-event data.","TYPE Original Research PUBLISHED 14 August 2024  \nDOI 10.3389/fdgth.2024.1443987  \nEDITED BY  \nRoberto Gatta,  \nUniversity of Brescia, Italy  \nREVIEWED BY  \nStefania Orini,  \nSan Giovanni di Dio Fatebenefratelli Center (IRCCS), Italy  \nLeonardo Nucciarelli,  \nCatholic University of the Sacred Heart, Italy  \n*CORRESPONDENCE  \nAndreas Trojan  \n [andreas.trojan@see-spital.ch](andreas.trojan@see-spital.ch)  \nRECEIVED 04 June 2024  \nACCEPTED 18 July 2024  \nPUBLISHED 14 August 2024  \nCITATION  \nTrojan A, Laurenzi E, Jüngling S, Roth S, Kiessling M, Atassi Z, Kadvany Y, Mannhart M, Jackisch C, Kullak-Ublick G and Witschel HF (2024) Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning.  \nFront. Digit. Health 6:1443987 .  \ndoi: 10.3389/fdgth.2024.1443987  \nCOPYRIGHT  \n© 2024 Trojan, Laurenzi, Jüngling, Roth, Kiessling, Atassi, Kadvany, Mannhart, Jackisch, Kullak-Ublick and Witschel. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTowards an early warning system for monitoring of cancer patients using hybrid interactive machine learning  \nAndreas Trojan 1,2*, Emanuele Laurenzi 3, Stephan Jüngling3, Sven Roth2, Michael Kiessling1, Ziad Atassi1, Yannick Kadvany4, Meinrad Mannhart5, Christian Jackisch 6, Gerd Kullak-Ublick2 and Hans Friedrich Witschel 3  \n1Oncology, Breast Center Zürichsee, Horgen, Switzerland, 2Clinic for Clinical Pharmacology and Toxicology, University Hospital, Zürich, Switzerland, 3FHNW, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland, 4Mobile Health AG, Zürich, Switzerland, 5Onko-Hämatologisches Zentrum Zug, Zug, Switzerland, 6Sana Klinikum Offenbach GmbH, Offenbach, Germany  \nBackground: The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions.  \nObjective: We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient’s individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations.  \nMethods: The prediction of unplanned visits was achieved by employing a whitebox ML algorithm (i.e., rule learner), which learned rules from patient data (i.e., ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms) . Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plaus","cbCait8sESO41bdS","https://ap.wps.com/l/cbCait8sESO41bdS","pdf",29606128,2,1,11,"English","en",105,"# Background\n# Objective\n# Methods\n## Data and model\n## Rule evaluation and expert review\n# Results\n# Conclusions","[{\"question\":\"What problem does the early warning system aim to solve?\",\"answer\":\"It predicts situations where supportive interventions become necessary to prevent unplanned visits during outpatient systemic treatment.\"},{\"question\":\"How does the hybrid interactive machine learning approach work?\",\"answer\":\"It uses a whitebox rule-learning algorithm to derive human-interpretable rules from ePROs, vital parameters, and free text, then ranks and validates them with oncological experts who review plausibility and adjust conditions.\"},{\"question\":\"What performance did the machine-learned rules achieve compared with a human baseline?\",\"answer\":\"On the full dataset, the rule set achieved recall of 19% and precision of 5%, while the human baseline achieved recall and precision of 0% for the adverse events recorded in the dataset.\"}]","Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning - Research study | PDF",1785942570,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"towards-an-early-warning-system-for-monitoring-of-cancer-patients-using-hybrid-interactive-machine-learning-research-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/towards-an-early-warning-system-for-monitoring-of-cancer-patients-using-hybrid-interactive-machine-learning-research-study/127882/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the early warning system aim to solve?","Question",{"text":76,"@type":77},"It predicts situations where supportive interventions become necessary to prevent unplanned visits during outpatient systemic treatment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the hybrid interactive machine learning approach work?",{"text":81,"@type":77},"It uses a whitebox rule-learning algorithm to derive human-interpretable rules from ePROs, vital parameters, and free text, then ranks and validates them with oncological experts who review plausibility and adjust conditions.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance did the machine-learned rules achieve compared with a human baseline?",{"text":85,"@type":77},"On the full dataset, the rule set achieved recall of 19% and precision of 5%, while the human baseline achieved recall and precision of 0% for the adverse events recorded in the dataset.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]