[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117165-en":3,"doc-seo-117165-105":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},117165,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Using machine learning for healthcare treatment planning - Original research","A patient-centric methodology applies explainable machine learning to recommend healthcare treatment plans, demonstrated via breast cancer care. Instead of limiting ML to diagnosis and early detection, the approach targets decision support for different disease severities by considering chemotherapy, radiation, combined chemotherapy with radiation, or no additional therapy beyond surgery. Real-world data from over 10,000 patients across six years is used to build classifiers. Emphasis goes beyond selecting a plan to providing explanations that can be defended to patients.","Boston University  \nOpenBU [http://open. bu.edu](http://open. bu.edu)  \n\n| BU Open Access Articles BU Open Access Articles |\n| --- |\n| 2023\u003Cbr>Using machine learning for healthcare treatment planning\u003Cbr>This work was made openly accessible by BU Faculty. Please share how this access benefits you.\u003Cbr>Your story matters. |\n\n\n| Version | Published version |\n| --- | --- |\n| Citation (published version): | S. Dubey, G. Tiwari, S. Singh, S. Goldberg, E. Pinsky. 2023. \"Using machine learning for healthcare treatment planning. \" Frontiers in Artificial Intelligence, Volume 6, pp.1124182- . [https://doi.org/10.3389/frai.2023.1124182](https://doi.org/10.3389/frai.2023.1124182) |\n\n[https://hdl.handle.net/2144/48818](https://hdl.handle.net/2144/48818)[ ](https://hdl.handle.net/2144/48818)Boston University  \nTYPE Original Research PUBLISHED 25 April 2023  \nDOI 10. 3389/frai.2023.1124182  \nOPEN ACCESS  \nEDITED BY  \nVladimir Brusic,  \nThe University of Nottingham Ningbo, China  \nREVIEWED BY  \nBayram Akdemir,  \nKonya Technical University, Türkiye Tianyi Qiu,  \nFudan University, China  \n*CORRESPONDENCE  \nEugene Pinsky  \n [epinsky@bu.edu](epinsky@bu.edu)  \nRECEIVED 14 December 2022  \nACCEPTED 03 April 2023  \nPUBLISHED 25 April 2023  \nCITATION  \nDubey S, Tiwari G, Singh S, Goldberg S and Pinsky E (2023) Using machine learning for healthcare treatment planning.  \nFront. Artif. Intell. 6:1124182 .  \ndoi: 10.3389/frai.2023.1124182  \nCOPYRIGHT  \n© 2023 Dubey, Tiwari, Singh, Goldberg and Pinsky. This is an open-access 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.  \nUsing machine learning for healthcare treatment planning  \nSnigdha Dubey1 , Gaurav Tiwari1 , Sneha Singh1 , Saveli Goldberg2 and Eugene Pinsky1*  \n1 Department of Computer Science, Metropolitan College, Boston University, Boston, MA, United States, 2 Department of Radiation Oncology Mass General Hospital, Boston, MA, United States  \nWe present a methodology for using machine learning for planning treatments. As a case study, we apply the proposed methodology to Breast Cancer. Most of the application of Machine Learning to breast cancer has been on diagnosis and early detection. By contrast, our paper focuses on applying Machine Learning to suggest treatment plans for patients with di􀀀erent disease severity. While the need for surgery and even its type is often obvious to a patient, the need for chemotherapy and radiation therapy is not as obvious to the patient. With this in mind, the following treatment plans were considered in this study: chemotherapy, radiation, chemotherapy with radiation, and none of these options (only surgery) . We use real data from more than 10,000 patients over 6 years that includes detailed cancer information, treatment plans, and survival statistics. Using this data set, we construct Machine Learning classifiers to suggest treatment plans. Our emphasis in this e􀀀ort is not only on suggesting the treatment plan but on explaining and defending a particular treatment choice to the patient.  \nKEYWORDS  \nmachine learning, ML in healthcare treatment, nearest neighbor classification, explainable AI, ML in healthcare environments  \n1. Introduction  \nBreast cancer is a leading cause of cancer-related deaths among women worldwide. Early detection and accurate breast cancer diagnosis are crucial for improving patient outcomesand reducing mortality rates. It is the most commonly diagnosed cancer type, accounting for 1 in 8 cancer diagnoses worldwide (CDC, 2022) . According to the World Health Organization, in 2020, there were about 2.3 million new cases of breast cancer globally and about 685,000 deat","cbCaicKV6WDZXmR0","https://ap.wps.com/l/cbCaicKV6WDZXmR0","pdf",2901965,1,15,"English","en",105,"# Introduction\n## Breast cancer and treatment options\n## Machine learning for treatment planning","[{\"question\":\"What is the document’s main goal in using machine learning?\",\"answer\":\"It presents a methodology that uses machine learning to plan treatments, with the emphasis on suggesting a plan and explaining and defending the chosen treatment to patients.\"},{\"question\":\"How does the study apply the methodology, and to what condition?\",\"answer\":\"The methodology is demonstrated using breast cancer as a case study, focusing on patients with different disease severity rather than only diagnosis and early detection.\"},{\"question\":\"What treatment options are considered in the treatment planning task?\",\"answer\":\"The study considers chemotherapy, radiation, chemotherapy with radiation, and none of these options (only surgery).\"}]","Using machine learning for healthcare treatment planning - 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