Recent Trends in Hospitalization for Acute Myocardial Infarction in Beijing: Increasing Overall Burden and a Transition From ST-Segment Elevation to Non-ST-Segment Elevation Myocardial Infarction in a Population-Based Study

Comparable data on trends of hospitalization rates for ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) remain unavailable in representative Asian populations.To examine the temporal trends of hospitalization for acute myocardial infarction (AMI) and its subtypes in Beijing.Patients hospitalized for AMI in Beijing from January 1, 2007 to December 31, 2012 were identified from the validated Hospital Discharge Information System. Trends in hospitalization rates, in-hospital mortality, length of stay (LOS), and hospitalization costs were analyzed by regression models for total AMI and for STEMI and NSTEMI separately. In total, 77,943 patients were admitted for AMI in Beijing during the 6 years, among whom 67.5% were males and 62.4% had STEMI. During the period, the rate of AMI hospitalization per 100,000 population increased by 31.2% (from 55.8 to 73.3 per 100,000 population) after age standardization, with a slight decrease in STEMI but a 3-fold increase in NSTEMI. The ratio of STEMI to NSTEMI decreased dramatically from 6.5:1.0 to 1.3:1.0. The age-standardized in-hospital mortality decreased from 11.2% to 8.6%, with a significant decreasing trend evident for STEMI in males and females (P < 0.001) and for NSTEMI in males (P = 0.02). The rate of percutaneous coronary intervention increased from 28.7% to 55.6% among STEMI patients. The total cost for AMI hospitalization increased by 56.8% after adjusting for inflation, although the LOS decreased by 1 day.The hospitalization burden for AMI has been increasing in Beijing with a transition from STEMI to NSTEMI. Diverse temporal trends in AMI subtypes from the unselected "real-world" data in Beijing may help to guide the management of AMI in China and other developing countries

Enabling the Possibility of creating a New Smart Resilient City in the Post-Pandemic Period

Beginning in early 2020, the COVID-19 pandemic sweeps across the globe. Despite the full lockdowns are being released deliberately and gradually in most parts of the world, social distancing is still needed in short-term and medium-term to mitigate the spread of coronavirus. These disruptive changes of life and work landscape bring the needs to reset the way how we use our cities and the opportunities to reshape the way how we manage our cities, which directly impact on the wellbeing during the post-pandemic period. For previous developed smart cities, it remains to be checked whether the historical data and the existing solutions during pre-pandemic still works in the post-pandemic situations. Faced with the post-pandemic situation that we have never seen before, the effectiveness of developed smart city solutions along with the applicability of adopted historical data must be re-evaluated and re-verified timely. This paper aims at providing a start point of enabling a future of resilient cities from the pre-pandemic to post-pandemic. Firstly, behaviour changes will be discussed. Then, the framework of the bidirectional interaction between human and cities will be established in this paper, and the mitigation measures based on digital innovation will be further provided that could guarantee the smart cities from the insufficient post-pandemic data. Future works and challenges will also be discussed. With the pervasive digital transformation of cities, the possibility of creating a more robust and smart resilient city is provided to maximally unleash the value of data, historical or recent, under a people-focused view

Co-training an improved recurrent neural network with probability statistic models for named entity recognition

© Springer International Publishing AG 2017. Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further

Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period

The emergence of COVID-19 pandemic is causing tremendous impact on our daily lives, including the way people interact with buildings. Leveraging the advances in machine learning and other supporting digital technologies, recent attempts have been sought to establish exciting smart building applications that facilitates better facility management and higher energy efficiency. However, relying on the historical data collected prior to the pandemic, the resulting smart building applications are not necessarily effective under the current ever-changing situation due to the drifts of data distribution. This paper investigates the bidirectional interaction between human and buildings that leads to dramatic change of building performance data distributions post-pandemic, and evaluates the applicability of typical facility management and energy management applications against these changes. According to the evaluation, this paper recommends three mitigation measures to rescue the applications and embedded machine learning algorithms from the data inconsistency issue in the post-pandemic era. Among these measures, incorporating occupancy and behavioural parameters as independent variables in machine learning algorithms is highlighted. Taking a Bayesian perspective, the value of data is exploited, historical or recent, pre- and post-pandemic, under a people-focused view

Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data

Chronic diseases like cancer and diabetes are major threats to human life. Understanding the distribution and progression of chronic diseases of a population is important in assisting the allocation of medical resources as well as the design of policies in preemptive healthcare. Traditional methods to obtain large scale indicators on population health, e.g., surveys and statistical analysis, can be costly and time-consuming and often lead to a coarse spatio-temporal picture. In this paper, we leverage a dataset describing the human mobility patterns of citizens in a large metropolitan area. By viewing local human lifestyles we predict the evolution rate of several chronic diseases at the level of a city neighborhood. We apply the combination of a collaborative topic modeling (CTM) and a Gaussian mixture method (GMM) to tackle the data sparsity challenge and achieve robust predictions on health conditions simultaneously. Our method enables the analysis and prediction of disease rate evolution at fine spatio-temporal scales and demonstrates the potential of incorporating datasets from mobile web sources to improve population health monitoring. Evaluations using real-world check-in and chronic disease morbidity datasets in the city of London show that the proposed CTM+GMM model outperforms various baseline methods