Browsing by Subject "Medical treatment"

Now showing 1 - 3 of 3
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    Design of a novel MRI compatible manipulator for image guided prostate interventions
    (IEEE, 2005-02) Krieger, A.; Susil, R. C.; Ménard, C.; Coleman, J. A.; Fichtinger, G.; Atalar, Ergin; Whitcomb, L. L.
    This paper reports a novel remotely actuated manipulator for access to prostate tissue under magnetic resonance imaging guidance (APT-MRI) device, designed for use in a standard high-field MRI scanner. The device provides three-dimensional MRI guided needle placement with millimeter accuracy under physician control. Procedures enabled by this device include MRI guided needle biopsy, fiducial marker placements, and therapy delivery. Its compact size allows for use in both standard cylindrical and open configuration MRI scanners. Preliminary in vivo canine experiments and first clinical trials are reported.
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    Estimating the chance of success in IVF treatment using a ranking algorithm
    (Springer, 2015) Güvenir, H. A.; Misirli, G.; Dilbaz, S.; Ozdegirmenci, O.; Demir, B.; Dilbaz, B.
    In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.
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    Gambler's ruin bandit problem
    (IEEE, 2017) Akbarzadeh, Nima; Tekin, Cem
    In this paper, we propose a new multi-armed bandit problem called the Gambler's Ruin Bandit Problem (GRBP). In the GRBP, the learner proceeds in a sequence of rounds, where each round is a Markov Decision Process (MDP) with two actions (arms): a continuation action that moves the learner randomly over the state space around the current state; and a terminal action that moves the learner directly into one of the two terminal states (goal and dead-end state). The current round ends when a terminal state is reached, and the learner incurs a positive reward only when the goal state is reached. The objective of the learner is to maximize its long-term reward (expected number of times the goal state is reached), without having any prior knowledge on the state transition probabilities. We first prove a result on the form of the optimal policy for the GRBP. Then, we define the regret of the learner with respect to an omnipotent oracle, which acts optimally in each round, and prove that it increases logarithmically over rounds. We also identify a condition under which the learner's regret is bounded. A potential application of the GRBP is optimal medical treatment assignment, in which the continuation action corresponds to a conservative treatment and the terminal action corresponds to a risky treatment such as surgery.