Spatio-temporal Graph Learning for Epidemic Prediction

The COVID-19 pandemic has posed great challenges to public health services, government agencies, and policymakers, raising huge social conflicts between public health and economic resilience. Policies such as reopening or closure of business activities are formulated based on scientific projections of infection risks obtained from infection dynamics models. Though most parameters in epidemic prediction service models can be set with domain knowledge of COVID-19, a key parameter, namely, human mobility, is often challenging to estimate due to complex spatio-temporal correlations and social contexts under escalating COVID-19 facilities. Moreover, how to integrate the various implicit features to accurately predict infectious cases is still an open issue. To address this challenge, we formulate the problem as a spatio-temporal network representation problem and propose STEP, a Spatio-Temporal Epidemic Prediction framework, to estimate pandemic infection risk of a city by integrating various real-world conditions (e.g., City Risk Index, climate, and medical conditions) into graph-structured data. We also employ a multi-head attention mechanism in representation learning to extract implicit features for a given city. Extensive experiments have been conducted upon the real-world dataset for 51 states (50 states and Washington, D.C.) of the USA. Experimental results show that STEP can yield more accurate pandemic infection risk estimation than baseline methods. Moreover, STEP outperforms other methods in both short-term and long-term prediction. [ABSTRACT FROM AUTHOR] Copyright of ACM Transactions on Intelligent Systems & Technology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Association of Race and Ethnicity With Incidence of Dementia Among Older Adults.

Importance: The racial and ethnic diversity of the US, including among patients receiving their care at the Veterans Health Administration (VHA), is increasing. Dementia is a significant public health challenge and may have greater incidence among older adults from underrepresented racial and ethnic minority groups. Objective: To determine dementia incidence across 5 racial and ethnic groups and by US geographical region within a large, diverse, national cohort of older veterans who received care in the largest integrated health care system in the US. Design, Setting, and Participants: Retrospective cohort study within the VHA of a random sample (5% sample selected for each fiscal year) of 1 869 090 participants aged 55 years or older evaluated from October 1, 1999, to September 30, 2019 (the date of final follow-up). Exposures: Self-reported racial and ethnic data were obtained from the National Patient Care Database. US region was determined using Centers for Disease Control and Prevention (CDC) regions from residential zip codes. Main Outcomes and Measures: Incident diagnosis of dementia (9th and 10th editions of the International Classification of Diseases). Fine-Gray proportional hazards models were used to examine time to diagnosis, with age as the time scale and accounting for competing risk of death. Results: Among the 1 869 090 study participants (mean age, 69.4 [SD, 7.9] years; 42 870 women [2%]; 6865 American Indian or Alaska Native [0.4%], 9391 Asian [0.5%], 176 795 Black [9.5%], 20 663 Hispanic [1.0%], and 1 655 376 White [88.6%]), 13% received a diagnosis of dementia over a mean follow-up of 10.1 years. Age-adjusted incidence of dementia per 1000 person-years was 14.2 (95% CI, 13.3-15.1) for American Indian or Alaska Native participants, 12.4 (95% CI, 11.7-13.1) for Asian participants, 19.4 (95% CI, 19.2-19.6) for Black participants, 20.7 (95% CI, 20.1-21.3) for Hispanic participants, and 11.5 (95% CI, 11.4-11.6) for White participants. Compared with White participants, the fully adjusted hazard ratios were 1.05 (95% CI, 0.98-1.13) for American Indian or Alaska Native participants, 1.20 (95% CI, 1.13-1.28) for Asian participants, 1.54 (95% CI, 1.51-1.57) for Black participants, and 1.92 (95% CI, 1.82-2.02) for Hispanic participants. Across most US regions, age-adjusted dementia incidence rates were highest for Black and Hispanic participants, with rates similar among American Indian or Alaska Native, Asian, and White participants. Conclusions and Relevance: Among older adults who received care at VHA medical centers, there were significant differences in dementia incidence based on race and ethnicity. Further research is needed to understand the mechanisms responsible for these differences.

Educational Anomaly Analytics: Features, Methods, and Challenges.

Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

Liver abscess in the caudate lobe caused by a fishbone and treated by laparoscopy: a case report.

BACKGROUND: Ingestion of fish bones leading to gastric perforation and inducing abscess formation in the caudate lobe of the liver is very rare. CASE PRESENTATION: A 67-year-old man presented to our hospital with a 2-day history of subxiphoid pain. There were no specific symptoms other than pain. Laboratory tests showed only an increase in the number and percentage of neutrophils. Contrast-enhanced Computerized tomography (CT) of the abdomen showed two linear dense opacities in the gastric cardia, one of which penetrated the stomach and was adjacent to the caudate lobe of the liver, with inflammatory changes in the caudate lobe. We finally diagnosed his condition as a caudate lobe abscess secondary to intestinal perforation caused by a fishbone based on the history and imaging findings. The patient underwent 3D laparoscopic partial caudate lobectomy, incision and drainage of the liver abscess, and fishbone removal. The procedure was successful and we removed the fishbone from the liver. The patient was discharged on the 9th postoperative day without other complications. CONCLUSIONS: Liver abscess caused by foreign bodies requires multidisciplinary treatment. Especially when located in the caudate lobe, we must detect and remove the cause of the abscess as early as possible. Foreign bodies that perforate the gastrointestinal tract can penetrate to the liver and cause abscess formation, as in this case. When exploring the etiology of liver abscesses, we should investigate the general condition, including the whole gastrointestinal tract.

Guidelines on the treatment with integrated traditional Chinese medicine and western medicine for severe coronavirus disease 2019.

Severe Coronavirus Disease 2019 (COVID-19) is characterized by numerous complications, complex disease, and high mortality, making its treatment a top priority in the treatment of COVID-19. Integrated traditional Chinese medicine (TCM) and western medicine played an important role in the prevention, treatment, and rehabilitation of COVID-19 during the epidemic. However, currently there are no evidence-based guidelines for the integrated treatment of severe COVID-19 with TCM and western medicine. Therefore, it is important to develop an evidence-based guideline on the treatment of severe COVID-19 with integrated TCM and western medicine, in order to provide clinical guidance and decision basis for healthcare professionals, public health personnel, and scientific researchers involved in the diagnosis, treatment, and care of COVID-19 patients. We developed and completed the guideline by referring to the standardization process of the "WHO handbook for guideline development", the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system, and the Reporting Items for Practice Guidelines in Healthcare (RIGHT).

Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution

COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic.