[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127977-en":3,"doc-seo-127977-105":30,"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":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},127977,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning","Smartphone-based ePRO collection during systemic cancer treatment can enable earlier detection of symptoms and therapy side effects, supporting timely adaptation while reducing adverse events and unplanned admissions. This work develops an Early Warning System that predicts situations requiring supportive interventions to prevent unplanned visits by analyzing dynamically collected standardized ePROs alongside vital parameters, medication, and free-text inputs. A hybrid model combines a white-box, human-interpretable rule learner with oncological expert review to address imbalanced data limitations and improve practical alert triggers.","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2024  \nTowards an early warning system for monitoring of cancer patients using hybrid  \ninteractive machine learning  \nTrojan, Andreas ; Laurenzi, Emanuele ; Jüngling, Stephan ; Roth, Sven ; Kiessling, Michael ; Atassi, Ziad ; Kadvany, Yannick ; Mannhart, Meinrad ; Jackisch, Christian ; Kullak-Ublick, Gerd ; Witschel, Hans Friedrich  \nDOI: [https://doi.org/10.3389/fdgth.2024.1443987](https://doi.org/10.3389/fdgth.2024.1443987)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-276080](https://doi.org/10.5167/uzh-276080)  \nJournal Article Published Version  \nThe following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0) License.  \nOriginally published at:  \nTrojan, Andreas; Laurenzi, Emanuele; Jüngling, Stephan; Roth, Sven; Kiessling, Michael; Atassi, Ziad; Kadvany, Yannick; Mannhart, Meinrad; Jackisch, Christian; Kullak-Ublick, Gerd; Witschel, Hans Friedrich (2024) . Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning. Frontiersin Digital Health, 6:1443987 .  \nDOI: [https://doi.org/10.3389/fdgth.2024.1443987](https://doi.org/10.3389/fdgth.2024.1443987)  \nTYPE 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 electro","cbCaihyBx4RhQaG5","https://ap.wps.com/l/cbCaihyBx4RhQaG5","pdf",596771,1,12,"English","en",105,"# Background\n# Objective\n# Methods\n## Prediction of unplanned visits via rule learning\n## Hybrid human-expert review\n# Results","[{\"question\":\"What problem does the proposed early warning system address?\",\"answer\":\"It aims to predict situations in which supportive interventions become necessary to prevent unplanned visits for cancer patients during systemic treatment.\"},{\"question\":\"Which data sources are used to build the hybrid ML model?\",\"answer\":\"The system uses dynamically collected standardized electronic patient-reported outcome (ePRO) data, together with vital parameters, medication, and free-text information captured via a smartphone app.\"},{\"question\":\"How does the hybrid approach combine machine learning and human expertise?\",\"answer\":\"A white-box rule learner generates human-interpretable rules that act as alert triggers, then oncological experts review the higher-priority rules for plausibility and extend them with additional conditions.\"}]","Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning | 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