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      Using machine learning for healthcare treatment planning

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          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.

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          Most cited references15

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          Nearest neighbor pattern classification

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            Pattern Recognition and Machine Learning

            Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same ?eld, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had signi?cant impact on both algorithms and applications. This new textbook re?ects these recent developments while providing a comp- hensive introduction to the ?elds of pattern recognition and machine learning. It is aimed at advanced undergraduates or ?rst year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not - sential as the book includes a self-contained introduction to basic probability theory.
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              A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.

              The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.
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                Author and article information

                Contributors
                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                25 April 2023
                2023
                : 6
                : 1124182
                Affiliations
                [1] 1Department of Computer Science, Metropolitan College, Boston University , Boston, MA, United States
                [2] 2Department of Radiation Oncology Mass General Hospital , Boston, MA, United States
                Author notes

                Edited by: Vladimir Brusic, The University of Nottingham Ningbo, China

                Reviewed by: Bayram Akdemir, Konya Technical University, Türkiye; Tianyi Qiu, Fudan University, China

                *Correspondence: Eugene Pinsky epinsky@ 123456bu.edu
                Article
                10.3389/frai.2023.1124182
                10167842
                37181733
                7a60d16c-9dea-48af-bb83-78fe6da33559
                Copyright © 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.

                History
                : 14 December 2022
                : 03 April 2023
                Page count
                Figures: 10, Tables: 9, Equations: 3, References: 18, Pages: 14, Words: 6162
                Categories
                Artificial Intelligence
                Original Research
                Custom metadata
                Medicine and Public Health

                machine learning,ml in healthcare treatment,nearest neighbor classification,explainable ai,ml in healthcare environments

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