Development of a territory-wide household-based composite index for measuring relative distribution of households by economic status in individual small areas throughout Hong Kong.

Background: Many countries have developed their country/nation-wide multidimensional area-based index on deprivation or socioeconomic status for resource allocation, service planning and research. However, whether each geographical unit proxied by a single index is sufficiently small to contain a relatively homogeneous population remains questionable. Globally, this is the first study that presents the distribution of domestic households by the territory-wide economic status index decile groups within each of the 2,252 small subunit groups (SSUGs) throughout Hong Kong, with a median study population of 1,300 and a median area of 42,400 m.

Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks.

Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as "golden" benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems.

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.

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

Purpose: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).

Materials And Methods: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives).

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.

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.

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).

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).

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.

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.

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,..

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).

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.