EURO-Online login
- New to EURO? Create an account
- I forgot my username and/or my password.
- Help with cookies
(important for IE8 users)
3065. An Application to Markov Decision Models in Healthcare Screening
Invited abstract in session MD-40: Stochastic Modelling, stream Advances in Stochastic Modelling and Learning Methods.
Monday, 14:30-16:00Room: 96 (building: 306)
Authors (first author is the speaker)
| 1. | Masayuki Horiguchi
|
| Department of Mathematics, Faculty of Science, Kanagawa University |
Abstract
In this talk, we consider the evaluation of periodic screening programme for woman breast cancer and formulate the model as a partially observable Markov decision process (POMDP). We convert a POMDP with finite state, observation state and action spaces to an equivalent completely observable MDP with continuous state and finite action spaces. By this approach, we have an optimal policy from dynamic programming (DP) equation in an equivalent MDP, but we focus on considering the evaluation in several scenarios of periodic screening for participants with silent condition of breast cancer and seeking an answer which programme is better than others for themselves. The aim of our research is, by using the data sets based on cancer registration and estimated parameters of survival rates and other ratios related to screening and diagnoses in Japan, to evaluate the validity of general recommendation with respect to the consideration of human health in scenarios of breast cancer screening programme in POMDP.
Keywords
- Stochastic Models
- Health Care
- Decision Analysis
Status: accepted
Back to the list of papers