3D dissimilar-siamese-u-net for hyperdense Middle cerebral artery sign segmentation.

The hyperdense middle cerebral artery sign (HMCAS) representing a thromboembolus has been declared as a vital CT finding for intravascular thrombus in the diagnosis of acute ischemia stroke. Early recognition of HMCAS can assist in patient triage and subsequent thrombolysis or thrombectomy treatment. A total of 624 annotated head non-contrast-enhanced CT (NCCT) image scans were retrospectively collected from multiple public hospitals in Hong Kong. In this study, we present a deep Dissimilar-Siamese-U-Net (DSU-Net) that is able to precisely segment the lesions by integrating Siamese and U-Net architectures. The proposed framework consists of twin sub-networks that allow inputs of left and right hemispheres in head NCCT images separately. The proposed Dissimilar block fully explores the feature representation of the differences between the bilateral hemispheres. Ablation studies were carried out to validate the performance of various components of the proposed DSU-Net. Our findings reveal that the proposed DSU-Net provides a novel approach for HMCAS automatic segmentation and it outperforms the baseline U-Net and many state-of-the-art models for clinical practice.

Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke.

BACKGROUND: The detection of large vessel occlusion (LVO) plays a critical role in the diagnosis and treatment of acute ischemic stroke (AIS). Identifying LVO in the pre-hospital setting or early stage of hospitalization would increase the patients' chance of receiving appropriate reperfusion therapy and thereby improve neurological recovery. METHODS: To enable rapid identification of LVO, we established an automated evaluation system based on all recorded AIS patients in Hong Kong Hospital Authority's hospitals in 2016. The 300 study samples were randomly selected based on a disproportionate sampling plan within the integrated electronic health record system, and then separated into a group of 200 patients for model training, and another group of 100 patients for model performance evaluation. The evaluation system contained three hierarchical models based on patients' demographic data, clinical data and non-contrast CT (NCCT) scans. The first two levels of modeling utilized structured demographic and clinical data, while the third level involved additional NCCT imaging features obtained from deep learning model. All three levels' modeling adopted multiple machine learning techniques, including logistic regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGboost). The optimal cut-off for the likelihood of LVO was determined by the maximal Youden index based on 10-fold cross-validation. Comparisons of performance on the testing group were made between these techniques. RESULTS: Among the 300 patients, there were 160 women and 140 men aged from 27 to 104 years (mean 76.0 with standard deviation 13.4). LVO was present in 130 (43.3%) patients. Together with clinical and imaging features, the XGBoost model at the third level of evaluation achieved the best model performance on testing group. The Youden index, accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) were 0.638, 0.800, 0.953, 0.684, 0.804, and 0.847, respectively. CONCLUSION: To the best of our knowledge, this is the first study combining both structured clinical data with non-structured NCCT imaging data for the diagnosis of LVO in the acute setting, with superior performance compared to previously reported approaches. Our system is capable of automatically providing preliminary evaluations at different pre-hospital stages for potential AIS patients.

An R package for analyzing and modeling ranking data.

BACKGROUND: In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis. RESULTS: Examples of the use of package pmr are given using a real ranking dataset from medical informatics, in which 566 Hong Kong physicians ranked the top five incentives (1: competitive pressures; 2: increased savings; 3: government regulation; 4: improved efficiency; 5: improved quality care; 6: patient demand; 7: financial incentives) to the computerization of clinical practice. The mean rank showed that item 4 is the most preferred item and item 3 is the least preferred item, and significance difference was found between physicians' preferences with respect to their monthly income. A multidimensional preference analysis identified two dimensions that explain 42% of the total variance. The first can be interpreted as the overall preference of the seven items (labeled as "internal/external"), and the second dimension can be interpreted as their overall variance of (labeled as "push/pull factors"). Various statistical models were fitted, and the best were found to be weighted distance-based models with Spearman's footrule distance. CONCLUSIONS: In this paper, we presented the R package pmr, the first package for analyzing and modeling ranking data. The package provides insight to users through descriptive statistics of ranking data. Users can also visualize ranking data by applying a thought multidimensional preference analysis. Various probability models for ranking data are also included, allowing users to choose that which is most suitable to their specific situations.

Bilinear probabilistic principal component analysis.

Probabilistic principal component analysis (PPCA) is a popular linear latent variable model for performing dimension reduction on 1-D data in a probabilistic manner. However, when used on 2-D data such as images, PPCA suffers from the curse of dimensionality due to the subsequently large number of model parameters. To overcome this problem, we propose in this paper a novel probabilistic model on 2-D data called bilinear PPCA (BPPCA). This allows the establishment of a closer tie between BPPCA and its nonprobabilistic counterpart. Moreover, two efficient parameter estimation algorithms for fitting BPPCA are also developed. Experiments on a number of 2-D synthetic and real-world data sets show that BPPCA is more accurate than existing probabilistic and nonprobabilistic dimension reduction methods.

A note on variational Bayesian factor analysis.

Existing works on variational bayesian (VB) treatment for factor analysis (FA) model such as [Ghahramani, Z., & Beal, M. (2000). Variational inference for Bayesian mixture of factor analysers. In Advances in neural information proceeding systems. Cambridge, MA: MIT Press; Nielsen, F. B. (2004). Variational approach to factor analysis and related models. Master's thesis, The Institute of Informatics and Mathematical Modelling, Technical University of Denmark.] are found theoretically and empirically to suffer two problems: (1) penalize the model more heavily than BIC and (2) perform unsatisfactorily in low noise cases as redundant factors can not be effectively suppressed. A novel VB treatment is proposed in this paper to resolve the two problems and a simulation study is conducted to testify its improved performance over existing treatments.

Fast ML estimation for the mixture of factor analyzers via an ECM algorithm.

In this brief, we propose a fast expectation conditional maximization (ECM) algorithm for maximum-likelihood (ML) estimation of mixtures of factor analyzers (MFA). Unlike the existing expectation-maximization (EM) algorithms such as the EM in Ghahramani and Hinton, 1996, and the alternating ECM (AECM) in McLachlan and Peel, 2003, where the missing data contains component-indicator vectors as well as latent factors, the missing data in our ECM consists of component-indicator vectors only. The novelty of our algorithm is that closed-form expressions in all conditional maximization (CM) steps are obtained explicitly, instead of resorting to numerical optimization methods. As revealed by experiments, the convergence of our ECM is substantially faster than EM and AECM regardless of whether assessed by central processing unit (CPU) time or number of iterations.

Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-GARCH) models.

We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

Modelling SARS data using threshold geometric process.

During the outbreak of an epidemic disease, for example, the severe acute respiratory syndrome (SARS), the number of daily infected cases often exhibit multiple trends: monotone increasing during the growing stage, stationary during the stabilized stage and then decreasing during the declining stage. Lam first proposed modelling a monotone trend by a geometric process (GP) [X(i), i=1,2,...] directly such that [a(i-1)X(i), i=1,2,...] forms a renewal process for some ratio a>0 which measures the direction and strength of the trend. Parameters can be conveniently estimated using the LSE methods. Previous GP models limit to data with only a single trend. For data with multiple trends, we propose a moving window technique to locate the turning point(s). The threshold GP model is fitted to the SARS data from four regions in 2003.

A comparative study of Chinese, American and Japanese nurses' perceptions of ethical role responsibilities.

This article reports a survey of nurses in different cultural settings to reveal their perceptions of ethical role responsibilities relevant to nursing practice. Drawing on the Confucian theory of ethics, the first section attempts to understand nursing ethics in the context of multiple role relationships. The second section reports the administration of the Role Responsibilities Questionnaire (RRQ) to a sample of nurses in China (n = 413), the USA (n = 163), and Japan (n = 667). Multidimensional preference analysis revealed the patterns of rankings given by the nurses to the statements they considered as important ethical responsibilities. The Chinese nurses were more virtue based in their perception of ethical responsibilities, the American nurses were more principle based, and the Japanese nurses were more care based. The findings indicate that the RRQ is a sensitive instrument for outlining the embedded sociocultural factors that influence nurses' perceptions of ethical responsibilities in the realities of nursing practice. This study could be important in the fostering of partnerships in international nursing ethics.

Incentives and barriers that influence clinical computerization in Hong Kong: a population-based physician survey.

OBJECTIVE: Given the slow adoption of medical informatics in Hong Kong and Asia, we sought to understand the contributory barriers and potential incentives associated with information technology implementation. DESIGN AND MEASUREMENTS: A representative sample of 949 doctors (response rate = 77.0%) was asked through a postal survey to rank a list of nine barriers associated with clinical computerization according to self-perceived importance. They ranked seven incentives or catalysts that may influence computerization. We generated mean rank scores and used multidimensional preference analysis to explore key explanatory dimensions of these variables. A hierarchical cluster analysis was performed to identify homogenous subgroups of respondents. We further determined the relationships between the sets of barriers and incentives/catalysts collectively using canonical correlation. RESULTS: Time costs, lack of technical support and large capital investments were the biggest barriers to computerization, whereas improved office efficiency and better-quality care were ranked highest as potential incentives to computerize. Cost vs. noncost, physician-related vs. patient-related, and monetary vs. nonmonetary factors were the key dimensions explaining the barrier variables. Similarly, within-practice vs external and "push" vs "pull" factors accounted for the incentive variables. Four clusters were identified for barriers and three for incentives/catalysts. Canonical correlation revealed that respondents who were concerned with the costs of computerization also perceived financial incentives and government regulation to be important incentives/catalysts toward computerization. Those who found the potential interference with communication important also believed that the promise of improved care from computerization to be a significant incentive. CONCLUSION: This study provided evidence regarding common barriers associated with clinical computerization. Our findings also identified possible incentive strategies that may be employed to accelerate uptake of computer systems.