Using machine learning for healthcare treatment planning
Files
Published version
Date
2023
Authors
Dubey, Snigdha
Tiwari, Gaurav
Singh, Sneha
Goldberg, Saveli
Pinsky, Eugene
Version
Published version
OA Version
Citation
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
Abstract
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.
Description
License
© 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.