Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop a...
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Cancer has been ranked first in the causes of death for 31 consecutive years in Taiwan. Radiofrequency ablation (RFA) is a treatment for hepatocellular carcinoma (HCC) and it becomes one of the important therapies for...
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Cancer has been ranked first in the causes of death for 31 consecutive years in Taiwan. Radiofrequency ablation (RFA) is a treatment for hepatocellular carcinoma (HCC) and it becomes one of the important therapies for HCC these years. For those who had HCC and were treated by RFA, their clinical data are collected to build predictive models which can be used in predicting the recurrence or not of liver cancer after RAF treatment. Clinical data with multiple measurements are merged based on different time periods and these data are further transformed based on temporal abstraction (TA). Data processed by TA reveal variations of clinical data with different time points. The goal of this study is to evaluate whether clinical data handled by TA could facilitate performance of predictive models. Different data sets are used in developing predictive models, including clinical data which are not processed by TA called the original data set, clinical data which are processed by TA called the TA data set, and combination of the original data set and the TA data set called the TA+original data set. Support vector machine (SVM) was selected as a classifier to develop predictive models. The results demonstrate data sets processed by TA provide benefit for predictive models.
Table of contents I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesti...
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Table of contents I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large s
Heavily-doped strained germanium (Ge) can emit light efficiently thanks to its pseudo direct band gap characteristic. This makes Ge a good candidate for on-chip monolithic light sources in silicon (Si) photonics syste...
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This paper presents the comparation of three different feature extraction techniques based on the Empirical Mode Decomposition (EMD) for a SSVEP-BCI. This approach based on the characterization of the signal by EMD, i...
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ISBN:
(纸本)9781479924004
This paper presents the comparation of three different feature extraction techniques based on the Empirical Mode Decomposition (EMD) for a SSVEP-BCI. This approach based on the characterization of the signal by EMD, is proposed as a novel alternative to other techniques and it was demonstrated that it exceeds both in accuracy rate and Information Transfer Rate (ITR). The experiments were performed in an offline way, and seven volunteers participated of the study. The stimulis were generated both by LCD and LEDs. The frequencies used were 8, 11, 13 and 15 Hz. The results here reported such represent the average of the seven participants, achieving a success rate of 81% and ITR of 23.32 bits/min of the total set of cases analyzed. It is further confirmed that the highest success rates and ITRs were obtained for stimulation by LEDs.
This paper presents the evaluation of seven techniques of feature extraction (PSD, F-Test, EMD, MCE, CCA, LASSO and MSI) for gaze-target detections in a SSVEP-based BCI. Two type of technologies for visual stimulation...
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This paper presents the evaluation of seven techniques of feature extraction (PSD, F-Test, EMD, MCE, CCA, LASSO and MSI) for gaze-target detections in a SSVEP-based BCI. Two type of technologies for visual stimulation were used (LCD and LEDs). Five differents windows lengths (1, 2, 4, 5 and 10 s) were used and seven volunteers participated in this study. The highest accuracy obtained in all cases was 93.57% using LEDs and the highest ITR was 36.90 bits/min for LCD. The technique based on MSI shows the highest success rate in both cases (LCD or LED) and is even more noticeable when the window size is increased.
To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a ...
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ISBN:
(纸本)9781424479276
To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold of orthogonal matrices in a semi-supervised manner. Subsequently, the feature space becomes more consistent across sessions and classifiers performance can be enhanced. Experimental results show that the proposed adaptation method yields improvements in classification performance.
The great development in the area of evolutionary algorithms in recent decades has increased the range of applications of these tools and improved its performance in different fronts. In particular, the Differential E...
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ISBN:
(纸本)9781479944606
The great development in the area of evolutionary algorithms in recent decades has increased the range of applications of these tools and improved its performance in different fronts. In particular, the Differential Evolution (DE) algorithm has proven to be a simple and efficient optimizer in several contexts. Despite of its success, its performance is closely related to the choice of variation operators and the parameters which control these operators. To increase the robustness of the method and the ease of use for the average user, the pursuit for methods of self-configuration has been increasing as well. There are several methods in the literature for setting parameters and operators. In order to understand the effects of these approaches on the performance of DE, this paper presents a thorough experimental analysis of the main existing paradigms. The results show that simple approaches are able to bring significant improvements to the performance of DE.
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