Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic ...processing of clinical texts, resulting in an improved quality of health-related data analysis. Driven by the 2010 i2b2/VA Challenge Evaluation, the relation recognition problem in EMRs has been studied by many researchers to address this important aspect of EMR information extraction.
This paper proposes an Attention-Based Deep Residual Network (ResNet) model to recognize medical concept relations in Chinese EMRs.
Our model achieves F
-score of 77.80% on the manually annotated Chinese EMRs corpus and outperforms the state-of-the-art approaches.
The residual network-based model can reduce the negative impact of corpus noise to parameter learning, and the combination of character position attention mechanism will enhance the identification features of different type of entities.
With the increasing urbanization rate, higher demand is placed on drainage systems, necessitating analysis of their risk profiles. While many studies focus on the individual hydraulic modeling of ...stormwater and wastewater systems when considering operational risks, they often overlook the exacerbating effects of combined stormwater and wastewater flows on each other’s risks. In this study, we constructed a model of the combined stormwater and wastewater drainage system in Lijiao, Guangdong Province. We analyzed the operational risks of both stormwater and sewage networks in different scenarios, focusing on full-load pipes, overload pipes, node overflows, and minimum flow velocities. Furthermore, we compared the exacerbating effects of sewage and stormwater on each other’s network operational risks. Simulation results indicated that, for sewage networks with combined sewer sections, the lengths of full-load pipes and overload pipes increased by an average of 27.4% and 16.3%, respectively, during rainy weather scenarios compared to dry weather. After considering the inflow of sewage into combined sewer sections, the average length of full-load pipes increased by 3.8 km, overload pipe length increased by 1.1 km, and overflow volume increased by 2.26 × 104 m3. As the return period of rainfall events increases, these inter-system impacts gradually diminish. Therefore, when modeling drainage systems and assessing operational risks, particularly during low return period rainfall events, the inclusion of both stormwater and sewage in combined sewer networks should be comprehensively considered.
Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the ...structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction.
Abstract
Over the past 30 years, water security issue in Chinese cities has become increasingly serious, largely due to rapid urbanization, population growth and disproportionate investment in water ...infrastructure. Urban rainwater harvesting (URWH), a household and community-level rainwater management measure, has been widely used during this period. This study provides a critical review of the policies, methods, technology, construction, implementation and benefits related to URWH in China. We show that URWH in China has gone through three phases over the past 30 years: an initial development phase, a rapid development phase and a sponge city construction phase. URWH research has focused mainly on rainwater harvesting, storage, utilization, management and other technologies, with limited attention to policy and benefit analysis. However, the scale of URWH construction and implementation in China remains small and needs further development. Currently, while URWH assessments cover social, ecological and environmental benefits, the economic benefits need to be further strengthened. The next step in URWH implementation should be to strengthen research and development of policy, legal and design standards. This study provides guidance for the implementation of URWH in Chinese cities and other cities alike in developing countries.
Electronic medical records (EMRs), such as hospital discharge summaries, contain a wealth of information only expressed in natural language. Automated methods for extracting information from these ...records must be able to recognize medical concepts in text and their semantic context. A contextual property critical to reason on information from EMRs is the doctor's belief status or assertion of the patient's medical problem. Research on the medical assertion classification (MAC) can establish the foundation for various health data analyses and clinical applications. However, previous MAC studies are mainly based on traditional machine learning methods which mostly require manually constructed features and the original unlabeled data cannot be easily and effectively applied to classification or classification tasks. Furthermore, external medical knowledge such as various medical dictionary bases, which provides rich explain and definition information about medical entity, is rarely utilized in existing neural network models of medical information extraction. In this study, we propose a deep neural network architecture enhanced by medical knowledge attention layer through combining GRU neural network with CNN model to classify the assertion type of medical problem such as disease and symptom in Chinese EMRs. The attention layer in the model is applied to integrate entity representations learned from medical dictionary bases as query for encoding. Experimental results on own manually annotated corpus indicate our approach achieves better performance compared to existing methods.
This article examines the outcomes of African American–led nonprofit organizations in improving the lives of the youth they serve. Using the framework of representative bureaucracy, this study ...suggests that demographic similarity among nonprofit leadership and nonprofit clients is transferable to increasing positive youth outcomes within African American–led youth-focused nonprofit organizations. Based on survey data from 727 youth, enhanced by qualitative interviews and focus groups, this research examines youth outcomes across three African American–led nonprofits and compares them with demographically similar youth in their communities. Youth outcomes are analyzed in the areas of academic performance, deviant behavior, family and social support, and self-esteem and resiliency. The findings suggest youth who participate in African American–led nonprofit organizations outperform their peers in the areas of academic performance and self-esteem. These outcomes are important because African American–led nonprofit organizations disproportionately serve African American youth who may not otherwise be served by other extracurricular programming.
The formation of an academic field is evidenced by many factors, including the growth of relevant research articles and the increasing impact of highly cited publications. Building upon recent ...scoping reviews of journal publications in STEM education, this study aimed to provide a systematic review of high impact empirical studies in STEM education to gain insights into the development of STEM education research paradigms. Through a search of the Web of Science core database, we identified the top 100 most-cited empirical studies focusing on STEM education that were published in journals from 2000 to 2021 and examined them in terms of various aspects, including the journals where they were published, disciplinary content coverage, research topics and methods, and authorship’s nationality/region and profession. The results show that STEM education continues to gain more exposure and varied disciplinary content with an increasing number of high impact empirical studies published in journals in various STEM disciplines. High impact research articles were mainly authored by researchers in the West, especially the United States, and indicate possible “hot” topics within the broader field of STEM education. Our analysis also revealed the increased participation and contributions from researchers in diverse fields who are working to formulate research agendas in STEM education and the nature of STEM education scholarship.
•A mathematical method is proposed to derive the sewage flow diurnal patterns.•Simulate the sewage pipe operation risk by infoworks ICM model.•Non-pharmaceutical interventions for COVID-19 affects ...municipal sewage flow.•Epidemic lockdown will increase sewage flow and pipe load in residential areas.•Homeworking will delay morning peak time and increase noon peak volume.
Non-Pharmaceutical Interventions (NPIs) have been widely employed globally over the past three years to control the rapid spread of coronavirus disease 2019 (COVID-19). These measures have imposed restrictions on urban residents’ activities and significantly influenced sewage discharge characteristics within sewage network, particularly in densely populated cities in China. This study focused on the nodal flow diurnal patterns and sewage network operational risks before and after epidemic lockdown in Beijing from March to May in 2022. Nodal flow diurnal patterns on weekdays and weekends before and after NPIs were analyzed using measured data through statistical and mathematical methods. A sewage network model was established to simulate and analyze the operational risks based on InfoWorks ICM before and after epidemic lockdown. The main conclusions were as follows: (1) In predominantly residential areas, the total wastewater volume increased by approximately 28.76 % to 33.52 % after the implementation of strict NPIs. The morning and midday “M” peaks on normalized weekdays transformed into “N” peaks, and the morning peak time was delayed by 0.5 to 1 hour after the lockdown; (2) Following NPIs, More than 90 % of manholes’ average water levels rose to varying degrees, approximately 50 % of pipe lengths exhibited a full flow state; (3) When the lockdown was in place during a hot summer day, sewage overflow phenomena were observed in 4.6 % and 9.6 % of manholes, respectively, with per capita daily drainage equivalent reaching 40–50 %. These findings hold significant implications for the proactive planning and operational management of water industry infrastructure during major emergencies.
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