Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Methods of Documentation VI: Case Management Model

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Disclosure of AI Use in Radiology: Patient Perspectives and Preferences.

Radiology·2026

Same author

Modeling developmental spiking behavior driven by ionic current dynamics of mouse and human inner hair cells using a calcium-enhanced Izhikevich framework.

Journal of computational neuroscience·2026

Same author

Structural analysis of <i>de novo</i> designed binders targeting the closed state of HSP90.

bioRxiv : the preprint server for biology·2026

Same author

Report on the detection of pathogenic Leptospira sp. in synanthropic rodents and Asian house shrews ( Suncus Murinus ) from Puducherry, India, 2022.

Journal of vector borne diseases·2026

Same author

Deciphering glyoxalase gene families and their coordinated regulation with the ascorbate-glutathione cycle during salinity stress in <i>Artemisia annua</i>.

Physiology and molecular biology of plants : an international journal of functional plant biology·2026

Same author

Unpacking X (formerly Twitter) discourse on fluoride and related topics during the 2024 US presidential election.

Journal of the American Dental Association (1939)·2026

Search research articles

Related Experiment Video

Updated: Jul 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Using machine learning for healthcare treatment planning.

Snigdha Dubey¹, Gaurav Tiwari¹, Sneha Singh¹

¹Department of Computer Science, Metropolitan College, Boston University, Boston, MA, United States.

Frontiers in Artificial Intelligence

|May 14, 2023

Summary

This summary is machine-generated.

This study introduces a machine learning approach to recommend personalized breast cancer treatment plans, moving beyond diagnosis to suggest therapies like chemotherapy and radiation based on patient data.

Keywords:

ML in healthcare environments ML in healthcare treatment explainable AI machine learning nearest neighbor classification

More Related Videos

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

Related Experiment Videos

Last Updated: Jul 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

Area of Science:

Oncology
Medical Informatics
Machine Learning

Background:

Machine learning applications in breast cancer predominantly focus on diagnosis and early detection.
Treatment decisions, particularly for chemotherapy and radiation, can be less clear to patients than surgical options.

Purpose of the Study:

To develop and apply a machine learning methodology for suggesting personalized breast cancer treatment plans.
To extend machine learning applications beyond diagnosis to treatment planning for varying disease severities.
To provide explanations for suggested treatment plans, aiding patient understanding and adherence.

Main Methods:

Utilized a dataset of over 10,000 breast cancer patients spanning 6 years.
Incorporated detailed cancer information, treatment regimens, and survival statistics.
Constructed machine learning classifiers to predict optimal treatment strategies.

Main Results:

Developed predictive models capable of suggesting treatment plans including chemotherapy, radiation, or both, alongside surgery.
Demonstrated the feasibility of using machine learning for complex treatment recommendations in oncology.
The methodology aims to support clinicians in treatment decision-making and patient communication.

Conclusions:

Machine learning offers a powerful tool for personalizing breast cancer treatment strategies.
The proposed methodology can enhance the clarity and justification of treatment recommendations for patients.
This approach has the potential to improve patient outcomes by optimizing therapy selection.