Introduction
Emerging evidence suggests that HbA1c variability, in addition to HbA1c itself, can be used as a predictor for mortality. The present study aims to examine the predictive power of mean ...HbA1c and HbA1c variability measures for diabetic complications as well as mortality.
Methods
The retrospective observational study analyzed diabetic patients who were prescribed insulin at outpatient clinics of the Prince of Wales Hospital and Shatin Hospital, Hong Kong, from 1 January to 31 December, 2009. Standard deviation (SD), root mean square (RMS), and coefficient of variation were used as measures of HbA1c variability. The primary outcomes were all-cause and cardiovascular mortality. Secondary outcomes were diabetes-related complications.
Results
The study cohort consists of 3424 patients, including 3137 patients with at least three HbA1c measurements. The low mean HbA1c subgroup had significantly shorter time-to-death for all-cause mortality (
P
< 0.001) but not cardiovascular mortality (
P
= 0.920). The high Hba1c subgroup showed shorter time-to-death for all-cause (
P
< 0.001) and cardiovascular mortality (
P
< 0.001). Mean Hba1c and Hba1c variability predicted all-cause as well as cardiovascular-specific mortality. In terms of secondary outcomes, mean HbA1c and HbA1c variability significantly predicted diabetic ketoacidosis/hyperosmolar hyperglycemic state/diabetic coma, neurological, ophthalmological, and renal complications. A significant association between dichotomized HbA1c variability and hypoglycemia frequency was found (
P
< 0.0001).
Conclusion
High HbA1c variability is associated with increased risk of all-cause and cardiovascular mortality, as well as diabetic complications. The association between hypoglycemic frequency, HbA1c variability, and mortality suggests that intermittent hypoglycemia resulting in poorer outcomes in diabetic patients.
Abstract
Our knowledge about the evolution of guarantee network in downturn period is limited due to the lack of comprehensive data of the whole credit system. Here we analyze the dynamic Chinese ...guarantee network constructed from a comprehensive bank loan dataset that accounts for nearly 80% total loans in China, during 01/2007-03/2012. The results show that, first, during the 2007-2008 global financial crisis, the guarantee network became smaller, less connected and more stable because of many bankruptcies; second, the stimulus program encouraged mutual guarantee behaviors, resulting in highly reciprocal and fragile network structure; third, the following monetary policy adjustment enhanced the resilience of the guarantee network by reducing mutual guarantees. Interestingly, our work reveals that the financial crisis made the network more resilient, and conversely, the government bailout degenerated network resilience. These counterintuitive findings can provide new insight into the resilience of real-world credit system under external shocks or rescues.
Research collaborations, especially long-distance and international collaborations, have become increasingly prevalent worldwide. Recent studies highlighted the significant role of research ...leadership in collaborations. However, existing measures of the research leadership do not take into account the intensity of leadership in the co-authorship network. More importantly, the spatial features, which influence the collaboration patterns and research outcomes, have not been incorporated in measuring the research leadership. To fill the gap, we construct an institution-level weighted co-authorship network that integrates two types of weight on the edges: the intensity of collaborations and the spatial score (the geographical distance adjusted by the cross-linguistic-border nature). Based on this network, we propose a novel metric, namely the spatial research leadership rank, to identify the leading institutions while considering both the collaboration intensity and the spatial features. The leadership of an institution is measured by the following three criteria: (a) the institution frequently plays the corresponding rule in papers with other institutions; (b) the institution frequently plays the corresponding rule in longer distance and even cross-linguistic-border collaborations; (c) the participating institutions led by the institution have high leadership status themselves. Harnessing a dataset of 323,146 journal publications in pharmaceutical sciences during 2010–2018, we perform a comprehensive analysis of the geographical distribution and dynamic patterns of research leadership flows at the institution level. The results demonstrate that the SpatialLeaderRank outperforms baseline metrics in predicting the scholarly impact of institutions. And the result remains robust in the field of Information Science and Library Science.
A large and growing body of "big data" is generated by internet search engines, such as Google. Because people often search for information about public health and medical issues, researchers may be ...able to use search engine data to monitor and predict public health problems, such as HIV. We sought to assess the feasibility of using Google search data to analyze and predict new HIV diagnoses cases in the United States.
From 2007 to 2014, we collected search volume data on HIV-related Google search keywords across the United States. State-level new HIV diagnoses data were collected from the Centers for Disease Control and Prevention (CDC) and AIDSVu.org. We developed a negative binomial model to predict HIV cases using a subset of significant predictor keywords identified by LASSO. The Google search data were combined with state-level HIV case reports provided by the CDC. We use historical data to train the model and predict new HIV diagnoses from 2011 to 2014, with an average R2 value of 0.99 between predicted versus actual cases, and average root-mean-square error (RMSE) of 108.75.
Results indicate that Google Trends is a feasible tool to predict new cases of HIV at the state level. We discuss the implications of integrating visualization maps and tools based on these models into public health and HIV monitoring and surveillance.
In this paper, a sentiment classification model is proposed to address two predominant issues in sentiment classification, namely domain-sensitive and data imbalance. Since words may embed distinct ...sentiment polarities in different contexts, sentiment classification is widely contended as a domain-sensitive task. Accordingly, this paper draws on label propagation to induce universal and domain-specific sentiment lexicons and builds a domain-adaptive sentiment classification model that incorporates universal and domain-specific knowledge into a unified learning framework. On the flip side, sentiment-related corpuses are usually formed with skewed polarity distribution because individuals tend to share similar assessment criteria on a given object and hence their sentiment polarities toward the same object are likely to be similar. We endeavor to address such imbalanced data problem by advancing a novel over-sampling technique. Unlike existing over-sampling approaches that generate minority-class samples from numerical feature space, the proposed sampling method directly creates synthetic texts from word spaces. Several experiments are conducted to verify the effectiveness of the proposed lexicon generation method, learning framework, and over-sampling method. Results show that the induced sentiment lexicons are interpretable and the proposed model is found to be effective for imbalanced and domain-specific text sentiment classification.
Machine learning (ML) algorithms “learn” information directly from data, and their performance improves proportionally with the number of high-quality samples. The aim of our systematic review is to ...present the state of the art regarding the implementation of ML techniques in the management of heart failure (HF) patients. We manually searched MEDLINE and Cochrane databases as well the reference lists of the relevant review studies and included studies. Our search retrieved 122 relevant studies. These studies mainly refer to (a) the role of ML in the classification of HF patients into distinct categories which may require a different treatment strategy, (b) discrimination of HF patients from the healthy population or other diseases, (c) prediction of HF outcomes, (d) identification of HF patients from electronic records and identification of HF patients with similar characteristics who may benefit form a similar treatment strategy, (e) supporting the extraction of important data from clinical notes, and (f) prediction of outcomes in HF populations with implantable devices (left ventricular assist device, cardiac resynchronization therapy). We concluded that ML techniques may play an important role for the efficient construction of methodologies for diagnosis, management, and prediction of outcomes in HF patients.
In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and ...organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems.
During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational ...information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.
Big Data for Social Transportation Zheng, Xinhu; Chen, Wei; Wang, Pu ...
IEEE transactions on intelligent transportation systems,
03/2016, Letnik:
17, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Big data for social transportation brings us unprecedented opportunities for resolving transportation problems for which traditional approaches are not competent and for building the next-generation ...intelligent transportation systems. Although social data have been applied for transportation analysis, there are still many challenges. First, social data evolve with time and contain abundant information, posing a crucial need for data collection and cleaning. Meanwhile, each type of data has specific advantages and limitations for social transportation, and one data type alone is not capable of describing the overall state of a transportation system. Systematic data fusing approaches or frameworks for combining social signal data with different features, structures, resolutions, and precision are needed. Second, data processing and mining techniques, such as natural language processing and analysis of streaming data, require further revolutions in effective utilization of real-time traffic information. Third, social data are connected to cyber and physical spaces. To address practical problems in social transportation, a suite of schemes are demanded for realizing big data in social transportation systems, such as crowdsourcing, visual analysis, and task-based services. In this paper, we overview data sources, analytical approaches, and application systems for social transportation, and we also suggest a few future research directions for this new social transportation field.
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