Carla Silva and Marcus Dahlem and Ins Dutra Quantum Markov Modeling for Healthcare
153 - 156
2018
12
8
International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering http://waset.org/publications/10009420
http://waset.org/publications/140
World Academy of Science, Engineering and Technology
A Markov model defines a system of states, composed
by the feasible transition paths between those states, and the
parameters of those transitions. The paths and parameters may be
a representative way to address healthcare issues, such as to identify
the most likely sequence of patient health states given the sequence
of observations. Furthermore estimating the length of stay (LoS) of
patients in hospitalization is one of the challenges that Markov models
allow us to solve. However, finding the maximum probability of
any path that gets to state at time t, can have high computational
cost. A quantum approach allows us to take advantage of quantum
computation since the calculated probabilities can be in several states,
ending up to outperform classical computing due to the possible
superposition of states when handling large amounts of data. The
aid of quantum physicsbased architectures and machine learning
techniques are therefore appropriated to address the complexity of
healthcare.
International Science Index 140, 2018