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Use of prescriptive analytics in the desing of dynamic treatment regimes for breast cancer.

Principal Investigator
Name
Gustavo Mendoza Olguin
Degrees
M.C.S.
Institution
Autonomous University of Puebla
Position Title
PhD Student
Email
About this CDAS Project
Study
HIPB (Learn more about this study)
Project ID
HIPB-9
Initial CDAS Request Approval
Jun 14, 2022
Title
Use of prescriptive analytics in the desing of dynamic treatment regimes for breast cancer.
Summary
The descriptive and predictive analytics have been used in diagnosis and detection of many types of cancer. In this project we will use prescriptive analytics obtained with reinforcement learning to develop a prescriptive-based methodology for data science using biomedical data. This project will use the provided information for environment and context modeling on machine learning techniques to design dynamic treatment regimes for breast cancer patients. The obtained treatments will be compared with those prescribed by physicians in order to evaluate accuracy.
Aims

* To use the prescriptive analytics obtained by reinforcement learning using a pragmatic perspective to optimize the benefits of each treatment for breast cancer.
* To design an expert system for medical specialist to select the best treatment for breast cancer confirmed patients.
* To use the prescriptive analytics obtained by reinforcement learning using a pragmatic approach to minimize the chemoterapy toxicity on patients that requires it.
* To use the prescriptive analytics obtained by reinforcement learning using a pragmatic approach to minimize the radiotherapy toxicity on patients that requires it.

Collaborators

Dra. Maria Josefa Somodevilla García - Autonomous University of Puebla
Dra. María de la Concepción Pérez de Celis Herrero - Autonomous University of Puebla
M.C. Yanin Chavarri Guerra - National Institute of Medical Sciences and Nutrition "Dr. Salvador Zubiran"