Browsing by Subject "Measurement accuracy"

Now showing 1 - 3 of 3

Open Access
Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation
(Springer, 2015) Ravens, U.; Katircioglu-Öztürk, D.; Wettwer, E.; Christ, T.; Dobrev, D.; Voigt, N.; Poulet, C.; Loose, S.; Simon, J.; Stein, A.; Matschke, K.; Knaut, M.; Oto, E.; Oto, A.; Güvenir, H. A.
Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.
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.
Open Access
A portable microfluidic system for rapid measurement of the erythrocyte sedimentation rate
(Royal Society of Chemistry, 2016) Isiksacan, Z.; Erel, O.; Elbuken, C.
The erythrocyte sedimentation rate (ESR) is a frequently used 30 min or 60 min clinical test for screening of several inflammatory conditions, infections, trauma, and malignant diseases, as well as non-inflammatory conditions including prostate cancer and stroke. Erythrocyte aggregation (EA) is a physiological process where erythrocytes form face-to-face linear structures, called rouleaux, at stasis or low shear rates. In this work, we proposed a method for ESR measurement from EA. We developed a microfluidic opto-electro-mechanical system, using which we experimentally showed a significant correlation (R2 = 0.86) between ESR and EA. The microfluidic system was shown to measure ESR from EA using fingerprick blood in 2 min. 40 μl of whole blood is filled in a disposable polycarbonate cartridge which is illuminated with a near infrared emitting diode. Erythrocytes were disaggregated under the effect of a mechanical shear force using a solenoid pinch valve. Following complete disaggregation, transmitted light through the cartridge was measured using a photodetector for 1.5 min. The intensity level is at its lowest at complete disaggregation and highest at complete aggregation. We calculated ESR from the transmitted signal profile. We also developed another microfluidic cartridge specifically for monitoring the EA process in real-time during ESR measurement. The presented system is suitable for ultrafast, low-cost, and low-sample volume measurement of ESR at the point-of-care.