[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128290-en":3,"doc-seo-128290-105":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},128290,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning–Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones","Prospective observational cohort study evaluating whether smartphone-measured step count changes can forecast an upcoming clinical adverse event in patients receiving systemic anticancer treatment. Patients’ physical activity was continuously monitored for 90 days, and clinical adverse events such as unplanned hospitalizations and treatment modifications were extracted from medical records. Prediction models were trained and validated using elastic net, random forest, and neural network approaches, with performance assessed using AUC. Results showed high accuracy for predicting unplanned hospitalizations within 7 days, while treatment modifications and other clinically relevant adverse events were not reliably predicted.","EUR Research Information Portal  \nMachine Learning–Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones  \nPublished in:  \nJCO clinical cancer informatics  \nPublication status and date:  \nPublished: 01/06/2025  \nDOI (link to publisher):  \n10.1200/CCI-25-00023  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY  \nCitation for the published version (APA):  \nBrouwer, C. G. , Bartelet, B. M. , Douma, J. A. J. , van Doorn, L. , Kuip, E. J. M. , Verheul, H. M. W. , & Buffart, L. M. (2025) . Machine Learning–Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones. JCO clinical cancer informatics, 9, Article e2500023 . [https://doi.org/10.1200/CCI-](https://doi.org/10.1200/CCI-)[ ](https://doi.org/10.1200/CCI-)[25-00023](25-00023)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nDownloaded from ascopubs .org by Erasmus Universiteit on July 18, 2025 from [145.005.180.005](145.005.180.005)[ ](145.005.180.005)Copyright © 2025 American Society of Clinical Oncology . All rights reserved .  \nOriginal Reports | Telehealth  \nMachine Learning–Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones  \nCalvin G. Brouwer, MD1 ; Branca M. Bartelet, MSc1; Joeri A.J. Douma, MD, PhD2; Leni van Doorn, PhD3 ; Evelien J.M. Kuip, MD, PhD4; Henk M.W. Verheul, MD, PhD3 ; and Laurien M. Buffart, PhD1   \nDOI [https://doi.org/10.1200/CCI-25-00023](https://doi.org/10.1200/CCI-25-00023)  \n\n| ABSTRACT |  |\n| --- | --- |\n| PURPOSE | This study aimed to investigate whether changes in step count, measured using patients’ own smartphones, could predict a clinical adverse event in the upcoming week in patients undergoing systemic anticancer treatments using machine learning models. |\n| METHODS | This prospective observational cohort study included patients with various cancer types receiving systemic anticancer treatment. Physical activity was monitored continuously using patients’ own smartphones, measuring daily step count for 90 days during treatment. Clinical adverse events (ie, unplanned hospitalizations and treatment modifications) were extracted from medical records. Models predicting adverse events in the upcoming 7 days were created using physical activity data from the preceding 2 weeks. Machine learning models (elastic net [EN], random forest [RF], and neural network [NN]) were trained and validated on a 70:30 split cohort. Model performance was evaluated using the AUC. |\n| RESULTS | Among the 76 patients analyzed (median age 61 [IQR, 53-69] years, 39 [51%] female), 11 (14%) were hospitalized during the ","cbCaiuMBOcOKjT3h","https://ap.wps.com/l/cbCaiuMBOcOKjT3h","pdf",548827,4,1,11,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the study designed to predict in patients receiving systemic anticancer treatment?\",\"answer\":\"The study assesses whether changes in smartphone-measured step count can predict a clinical adverse event in the upcoming week, focusing on unplanned hospitalizations and treatment modifications.\"},{\"question\":\"How were step count data and adverse events obtained?\",\"answer\":\"Physical activity was continuously monitored with patients’ own smartphones to measure daily step count for 90 days, while clinical adverse events were extracted from medical records.\"},{\"question\":\"Which machine learning models performed best for upcoming hospitalizations?\",\"answer\":\"Random forest achieved the highest reported accuracy (AUC ~0.88), followed by neural network (AUC ~0.84) and elastic net (AUC ~0.83) for predicting unplanned hospitalizations within 7 days.\"}]","Machine Learning–Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones | 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