Is Pre-consultation Conducted by the Assistant Physician Effective in Improving Online Healthcare Service Quality and Satisfaction?

To improve online healthcare quality and efficiency, online healthcare communities (OHCs) enabled the pre-consultation function, in which an assistant physician interacts with the patient to understand and document the patient’s health conditions, medical history, and consultation objectives prior to the formal online consultation with the attending physician. Using detailed service data from a Chinese OHC, this study scrutinizes the effect of using pre-consultation on online healthcare service quality and satisfaction. The results show that pre-consultation can significantly increase the attending physician’s response speed, length, and the level of informational support embedded within the response, while maintaining a consistent level of emotional support. Despite the improvement in service quality, pre-consultation leads to decreased patient satisfaction with the consultation service. Furthermore, we find that pre-consultation improves service quality by enhancing the professionalism and comprehensiveness of patient case information and reducing information seeking and clarification of the attending physician with the patient

MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification

Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line

An Interactive Color Picker that Ensures WCAG2.0 Compliant Color Contrast Levels

AbstractInsufficient contrast between text and the background is a common problem on the web. WCAG2.0 addresses this problem, but the definition is hard to understand for most designers. Therefore, some web designers check their designs with contrast checking tools after the design is finished. If the design does not meet the WCAG2.0 guidelines the designer will have to go back and make adjustments. To overcome this problem a color picker tool is proposed that allows designers to select WCAG2.0 compliant colors during the design process thus eliminating the need for post-design color adjustments. First, the designer selects the first color freely from all available colors. Subsequently, only colors are presented that meets the chosen contrast level. In addition to being a design tool, it also serves as a pedagogical visualization aid that can help students and designers better understand the complex relationships between colors palettes and their contrasts

The Effect of Online Follow-up Services on Offline and Online Physician Demand: Evidence from Chronic Disease Physicians

The adoption of online follow-up services by physicians provides their offline patients with an important channel for medical follow-ups. Using detailed service data from a Chinese online healthcare community (OHC), the present study scrutinizes the rarely studied effect of adopting online follow-up services on offline and online physician demand in the context of chronic disease. The results demonstrate that adopting online follow-up services leads to higher offline physician demand. Interestingly, in contrast to the channel substitution effect documented in the literature, we find that providing online follow-up services also increases online physician demand. Furthermore, the results of mechanism tests reveal that online follow-up services affect online demand by boosting physicians’ online exposure and increasing the availability of information on their online service characteristics to patients. Our findings offer strategic guidance for physicians, design implications for OHCs, and insights for healthcare policymakers

JGAT: a joint spatio-temporal graph attention model for brain decoding

The decoding of brain neural networks has been an intriguing topic in neuroscience for a well-rounded understanding of different types of brain disorders and cognitive stimuli. Integrating different types of connectivity, e.g., Functional Connectivity (FC) and Structural Connectivity (SC), from multi-modal imaging techniques can take their complementary information into account and therefore have the potential to get better decoding capability. However, traditional approaches for integrating FC and SC overlook the dynamical variations, which stand a great chance to over-generalize the brain neural network. In this paper, we propose a Joint kernel Graph Attention Network (JGAT), which is a new multi-modal temporal graph attention network framework. It integrates the data from functional Magnetic Resonance Images (fMRI) and Diffusion Weighted Imaging (DWI) while preserving the dynamic information at the same time. We conduct brain-decoding tasks with our JGAT on four independent datasets: three of 7T fMRI datasets from the Human Connectome Project (HCP) and one from animal neural recordings. Furthermore, with Attention Scores (AS) and Frame Scores (FS) computed and learned from the model, we can locate several informative temporal segments and build meaningful dynamical pathways along the temporal domain for the HCP datasets. The URL to the code of JGAT model: https://github.com/BRAINML-GT/JGAT

Neyman-pearson classiffication under high-dimensional settings

Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error and a constrained type I error under a user specified level. This article is the first attempt to construct classifiers with guaranteed theoretical performance under the NP paradigm in high-dimensional settings. Based on the fundamental Neyman-Pearson Lemma, we used a plug-in approach to construct NP-Type classifiers for Naive Bayes models. The proposed classifiers satisfy the NP oracle inequalities, which are natural NP paradigm counterparts of the oracle inequalities in classical binary classification. Besides their desirable theoretical properties, we also demonstrated their numerical advantages in prioritized error control via both simulation and real data studies

Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning

Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted environments. Recent advancements in learning from demonstrations have shown promising results for robot learning in complex environments. While imitation learning of expert policies has been well-explored, the study of learning expert reward functions is largely under-explored in legged locomotion. This paper brings state-of-the-art Inverse Reinforcement Learning (IRL) techniques to solving bipedal locomotion problems over complex terrains. We propose algorithms for learning expert reward functions, and we subsequently analyze the learned functions. Through nonlinear function approximation, we uncover meaningful insights into the expert's locomotion strategies. Furthermore, we empirically demonstrate that training a bipedal locomotion policy with the inferred reward functions enhances its walking performance on unseen terrains, highlighting the adaptability offered by reward learning