Using machine learning for healthcare treatment planning
We 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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2023.1124182/full |
| _version_ | 1797839611268956160 |
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| author | Snigdha Dubey Gaurav Tiwari Sneha Singh Saveli Goldberg Eugene Pinsky |
| author_facet | Snigdha Dubey Gaurav Tiwari Sneha Singh Saveli Goldberg Eugene Pinsky |
| author_sort | Snigdha Dubey |
| collection | DOAJ |
| description | We 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 different 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 effort is not only on suggesting the treatment plan but on explaining and defending a particular treatment choice to the patient. |
| first_indexed | 2024-04-09T16:00:49Z |
| format | Article |
| id | doaj.art-7a86377e79a949d199627d9660851f67 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-09T16:00:49Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-7a86377e79a949d199627d9660851f672023-04-25T12:26:25ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122023-04-01610.3389/frai.2023.11241821124182Using machine learning for healthcare treatment planningSnigdha Dubey0Gaurav Tiwari1Sneha Singh2Saveli Goldberg3Eugene Pinsky4Department of Computer Science, Metropolitan College, Boston University, Boston, MA, United StatesDepartment of Computer Science, Metropolitan College, Boston University, Boston, MA, United StatesDepartment of Computer Science, Metropolitan College, Boston University, Boston, MA, United StatesDepartment of Radiation Oncology Mass General Hospital, Boston, MA, United StatesDepartment of Computer Science, Metropolitan College, Boston University, Boston, MA, United StatesWe 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 different 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 effort is not only on suggesting the treatment plan but on explaining and defending a particular treatment choice to the patient.https://www.frontiersin.org/articles/10.3389/frai.2023.1124182/fullmachine learningML in healthcare treatmentnearest neighbor classificationexplainable AIML in healthcare environments |
| spellingShingle | Snigdha Dubey Gaurav Tiwari Sneha Singh Saveli Goldberg Eugene Pinsky Using machine learning for healthcare treatment planning Frontiers in Artificial Intelligence machine learning ML in healthcare treatment nearest neighbor classification explainable AI ML in healthcare environments |
| title | Using machine learning for healthcare treatment planning |
| title_full | Using machine learning for healthcare treatment planning |
| title_fullStr | Using machine learning for healthcare treatment planning |
| title_full_unstemmed | Using machine learning for healthcare treatment planning |
| title_short | Using machine learning for healthcare treatment planning |
| title_sort | using machine learning for healthcare treatment planning |
| topic | machine learning ML in healthcare treatment nearest neighbor classification explainable AI ML in healthcare environments |
| url | https://www.frontiersin.org/articles/10.3389/frai.2023.1124182/full |
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