Browsing by Subject "Clinical decision support systems"

Now showing 1 - 3 of 3

Open Access
Adaptive ensemble learning with confidence bounds for personalized diagnosis
(AAAI Press, 2016) Tekin, Cem; Yoon, J.; Van Der Schaar, M.
With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in personalized diagnosis, massive amounts of distributed, heterogeneous, correlated and high-dimensional patient data from different sources such as wearable sensors, mobile applications, Electronic Health Record (EHR) databases etc. need to be processed. This requires learning both locally and globally due to privacy constraints and/or distributed nature of the multimodal medical data. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift.
Open Access
Diabetes management VIA gaussian process bandits
(2021-10) Çelik, Ahmet Alparslan
Management of chronic diseases such as diabetes mellitus requires adaptation of treatment regimes based on patient characteristics and response. There is no single treatment that ﬁts all patients in all contexts; moreover, the set of admissible treatments usually varies over the course of the disease. In this thesis, we address the problem of optimizing treatment regimes under time-varying constraints by using volatile contextual Gaussian process bandits. In particular, we propose a variant of GP-UCB with volatile arms, which takes into account the patient’s context together with the set of admissible treatments when recommending new treatments. Our Bayesian approach is able to provide treatment recommendations to the patients along with conﬁdence scores which can be used for risk assessment. We use our algorithm to recommend bolus insulin doses for type 1 diabetes mellitus patients. We test our algorithm on in-silico subjects that come with open source implementation of the FDA-approved UVa/Padova type 1 diabetes mellitus simulator. We also compare its performance against a clinician. Moreover, we present a pilot study with a few clinicians and patients, where we design interfaces that they can interact with the model. Meanwhile, we address issues regarding privacy, safety, and ethics. Simulation studies show that our algorithm compares favorably with traditional blood glucose regulation methods.
Open Access
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.