Enhancing Healthcare Quality with Reinforcement Learning Modeling

Gaddi Blumrosen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Optimizing healthcare efficiency is a major concern today. Currently, medical diagnostics can be inaccurate, and when translated to medical treatment strategies, like choosing if to operate or not, and which drag type and optimal dose, it might affect the medical treatment effectivity. Predictive models can be used for the design of treatment plan, which is a challenging task due to large number of parameters and the nonlinearity of the problem. A major challenge in these models is to model the natural subject response - spontaneous and in respond to the treatment. In this work, we suggest design of predictive models that can incorporate in optimal manner the human internal mechanism for his/her healthcare maintenance to a medical expert system by using reinforcement learning model with natural and medical agents. The personal and public medical records, medical knowledge, sensory inputs, including brain recording when available, can be used not just as constraints to the medical agent, but to estimate the natural agent states and reward. This new model suggestion has a potential to improve healthcare quality, with better control of the desired level of medical intervention.

Original languageEnglish
Title of host publication2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116341
StatePublished - Oct 2019
Event18th IEEE Sensors, SENSORS 2019 - Montreal, Canada
Duration: 27 Oct 201930 Oct 2019

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference18th IEEE Sensors, SENSORS 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • healthcare
  • medical diagnostics
  • reinforcement learning


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