Demand analysis of general practice patients for teaching clinic based on Kano model

BackgroundGeneral practice teaching clinics play a crucial role in the training of general practitioners, as they are more likely to enhance reception skills compared to traditional training methods. The quality of teaching clinics is largely determined by the level of patient acceptance. In recent years, the Kano model has become increasingly popular in the healthcare industry and has been used to enhance patient satisfaction. The objective of this study is to apply the Kano model to investigate the needs of patients in general practice teaching clinics and to rank the significance of each demand. This study will serve as a reference for enhancing the service quality of teaching clinics and advancing the field of general practice.MethodsA total of 101 patients of general practice at the Affiliated Hospital of Yangzhou University in Jiangsu province were selected using a random convenience sampling method to participate in a questionnaire survey. The questionnaire was designed by members of our team and was based on the Kano model. The study defined the service demand, assessed the impact of both satisfaction and dissatisfaction and created a matrix bubble diagram.ResultsThe study findings revealed that out of the 14 items of the general practice teaching clinic service demands, 1 item was categorized as a must-be requirement, 4 items were categorized as one-dimensional requirements, 2 items were categorized as an attractive requirement, 2 items were categorized as an indifferent requirement, and 5 items were categorized as mixed attributes. The findings of the matrix analysis showed that 4 items were situated in the area of one-dimensional attributes quadrant, 3 items were situated in the area of attractive attributes quadrant, 5 items were situated in the area of indifferent attributes quadrant, and 2 items were situated in the area of must-be attributes quadrant.ConclusionThe patients of general practice have positive attitudes toward teaching clinics. The findings can offer valuable insights for enhancing the quality of service and patient experience in general practice teaching clinics as well as for advancing the field of general practice

Studying Spread Patterns of COVID-19 based on Spatiotemporal Data

The current COVID-19 epidemic have transformed every aspect of our lives, especially our behavior and routines. These changes have been drastically impacting the economy in each region, such as local restaurants and transportation systems. With massive amounts of ambient data being collected everywhere, we now can develop innovative algorithms to have a much greater understanding of epidemic spread patterns of COVID-19 based on spatiotemporal data. The findings will open up the possibility to design adaptive planning or scheduling systems that will help preventing the spread of COVID-19 and other infectious diseases. In this tutorial, we will review the trending state-of-theart machine learning techniques to model epidemic spread patterns with spatiotemporal data. These techniques are organized from two aspects: (1) providing a comprehensive review of recent studies about human routine behavior modeling, such as inverse reinforcement learning and graph neural network, and the impacts of behaviors on the spread patterns of infectious diseases based on GPS data; (2) introducing the existing literature on using remote sensing data to monitor the spatiotemporal pattern of the epidemic spread. Under current epidemic with unknown lasting time, we believe that modeling the spread patterns of COVID-19 epidemic is an important topic that will benefit to researchers and practitioners from both academia and industry

XFlow: Benchmarking Flow Behaviors over Graphs

The occurrence of diffusion on a graph is a prevalent and significant phenomenon, as evidenced by the spread of rumors, influenza-like viruses, smart grid failures, and similar events. Comprehending the behaviors of flow is a formidable task, due to the intricate interplay between the distribution of seeds that initiate flow propagation, the propagation model, and the topology of the graph. The study of networks encompasses a diverse range of academic disciplines, including mathematics, physics, social science, and computer science. This interdisciplinary nature of network research is characterized by a high degree of specialization and compartmentalization, and the cooperation facilitated by them is inadequate. From a machine learning standpoint, there is a deficiency in a cohesive platform for assessing algorithms across various domains. One of the primary obstacles to current research in this field is the absence of a comprehensive curated benchmark suite to study the flow behaviors under network scenarios. To address this disparity, we propose the implementation of a novel benchmark suite that encompasses a variety of tasks, baseline models, graph datasets, and evaluation tools. In addition, we present a comprehensive analytical framework that offers a generalized approach to numerous flow-related tasks across diverse domains, serving as a blueprint and roadmap. Drawing upon the outcomes of our empirical investigation, we analyze the advantages and disadvantages of current foundational models, and we underscore potential avenues for further study. The datasets, code, and baseline models have been made available for the public at: https://github.com/XGraphing/XFlo

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting