Predicting Prescriptions via DSCA-Dual Sequences with Cross Attention Network

Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. However, CNN is not suitable for modeling the long-term context dependencies between speech signal frames. Recently, the recurrent neural networks (RNNs) have shown great abilities for modeling long-term context dependencies. However, the performance of RNNs is not good for low-resource speech recognition tasks, and is even worse than the conventional feed-forward neural networks. Moreover, these networks often overfit severely on the training corpus in the low-resource speech recognition tasks. This paper presents the results of our contributions to combine CNN and conventional RNN with gate, highway, and residual networks to reduce the above problems. The optimal neural network structures and training strategies for the proposed neural network models are explored. Experiments were conducted on the Amharic and Chaha datasets, as well as on the limited language packages (10-h) of the benchmark datasets released under the Intelligence Advanced Research Projects Activity (IARPA) Babel Program. The proposed neural network models achieve 0.1–42.79% relative performance improvements over their corresponding feed-forward DNN, CNN, bidirectional RNN (BRNN), or bidirectional gated recurrent unit (BGRU) baselines across six language collections. These approaches are promising candidates for developing better performance acoustic models for low-resource speech recognition tasks.

BackgroundTertiary hospitals in China are confronted with significant challenges due to limited spatial capacity and workforce constraints, leading to saturated allocation of medical resources and restricted growth in medical service provision. The incorporation of digital health into medical service process reengineering (MSPR) marks a pivotal transformation and restructuring of conventional health service delivery models. Specifically, the application of MSPR to blood sampling services processes reengineering (BSSPR) holds promise for substantially enhancing the efficiency and quality of medical services through streamlining and optimizing these procedures. However, the comprehensive impact of BSSPR has been infrequently quantified in existing research.ObjectiveThis study aims to investigate the influence of BSSPR on the efficiency and quality of medical services and to elucidate the key informative technological support points underpinning BSSPR.MethodsData were collected from both the new and old laboratory information systems from August 1, 2019, to December 31, 2021. A combination of statistical description, chi-square test, and t test was used to compare check-in time and waiting time of outpatients before and after the implementation of BSSPR. An interrupted time-series design was used to analyze the impact of BSSPR on medical service efficiency and quality, enabling the control of confounding variables, including changes in medical human resources and both long- and short-term temporal trends.ResultsBSSPR had an impact on the efficiency and quality of medical services. Notably, there was a significant increase in the number of patients receiving blood sampling services, with a daily service volume increase of ~150 individuals (P=.04). The average waiting time for patients decreased substantially from 29 (SD 36) to 11 (SD 11) minutes, indicating a marked improvement in patient experience. During the peak period, the number of patients receiving blood sampling services per working hour statistically increased from 9.56 to 16.77 (P<.001). The interrupted time-series model results demonstrated a reduction in patients’ waiting time by an average of 26.1 (SD 3.8; 95% CI –33.64 to –18.57) minutes. Although there was an initial decline in the number of outpatients admitted following BSSPR implementation, an upward trend was observed over time (β=1.13, 95% CI 0.91-1.36).ConclusionsBSSPR implementation for outpatients not only reduced waiting time and improved patients’ experience but also augmented the hospital’s capacity to provide medical services. This study’s findings offer valuable insights into the potential advantages of BSSPR and underscore the significance of harnessing digital technologies to optimize medical service processes. This research serves as a foundational basis and provides scientific support for the promotion and application of BSSPR in other health care contexts. By continuing to explore and refine the integration of digital technologies in health care, we can further enhance patient outcomes and elevate the overall quality of medical services.

PurposeAs an important management method of the global healthcare system, diagnosis related groups (DRGs) classify patients into different cost groups and pay more attention to the equitable distribution of medical resources and the quality of medical services. At present, most countries have used DRGs to help medical institutions and doctors to treat patients more accurately, avoid the waste of medical resources, and improve treatment efficiency.MethodsThe Web of Science database was searched to collect all relevant literature on DRGs from 2013 to 2022. The literature information was imported into CiteSpace, Vosviewer, and Histcite for data analysis and visualization of the results. Analyze the cooperative relationship among the countries, institutions, journals, and authors. The usage trend of keywords; Highlight the content of the cited articles.ResultsThe number of articles published in this decade was stable, and the number of citations in 2014 was the highest. The United States and Germany, as the first countries to use the DRGs system, are ahead of other countries in terms of the number and quality of articles. We have carried out content research on the articles with high citations, and summarized the application range of DRGs; classification method; advantages and disadvantages of the application. In general, the development trend of DRGs in foreign countries is to continuously optimize the classification method, expand the scope of application, and improve the application effect. These provide support and reference for the improvement of medical services and the perfection of the medical insurance system.ConclusionThe application of DRGs can improve the quality and efficiency of medical services, and reduce the waste of medical expenses. It can also promote the rational allocation of medical resources and the equity of medical services. In the future, DRGs will pay more attention to the personalized diagnosis and treatment and fine management of patients, and the sharing and standardization of medical data, to promote the development of medical informatization.

Due to the simple design pipeline, end-to-end (E2E) neural models for speech enhancement (SE) have attracted great interest. In order to improve the performance of the E2E model, the local and sequential properties of speech should be efficiently taken into account when modelling. However, in most current E2E models for SE, these properties are either not fully considered or are too complex to be realized. In this letter, we propose an efficient E2E SE model, termed WaveCRN. Compared with models based on convolutional neural networks (CNN) or long short-term memory (LSTM), WaveCRN uses a CNN module to capture the speech locality features and a stacked simple recurrent units (SRU) module to model the sequential property of the locality features. Different from conventional recurrent neural networks and LSTM, SRU can be efficiently parallelized in calculation, with even fewer model parameters. In order to more effectively suppress noise components in the noisy speech, we derive a novel restricted feature masking approach, which performs enhancement on the feature maps in the hidden layers; this is different from the approaches that apply the estimated ratio mask to the noisy spectral features, which is commonly used in speech separation methods. Experimental results on speech denoising and compressed speech restoration tasks confirm that with the SRU and the restricted feature map, WaveCRN performs comparably to other state-of-the-art approaches with notably reduced model complexity and inference time.

Finite-time synchronization control of a class of memristor-based recurrent neural networks

In blast furnace (BF) ironmaking process, the silicon content of hot metal is regarded as an important index to evaluate the stability of blast furnace and the quality of pig iron. In reality, the reactions inside BF with the features of dynamic, nonlinear and hysteretic impede accurate mathematical modeling. Data-driven models predict silicon content variation tendency through the analysis of historical data, which have achieved beneficial results. However, existing data-driven models are inefficient to handle time series specifically. Therefore, this paper proposes a recurrent neural network (RNN) which is apt at processing sequential data for silicon content prediction. A Long short-term memory (LSTM) unit is introduced to solve long-term dependency problems in conventional recurrent neural networks. Experiment results indicate that LSTM-RNN shows stronger adaptability and higher prediction accuracy at 86%-94% than that of other data-driven models, which is of great significance for controling the silicon content of hot metal.

In this paper, for the first time, the deep gated recurrent unit (Deep GRU) is used as a new macromodeling approach for nonlinear circuits. Similar to Long Short-Term Memory (LSTM), the GRU has gating units that control the information flow and makes the network less prone to the vanishing gradient problem. Having a smaller number of gates causes GRU to have fewer parameters compared to LSTM leading to better model accuracy. Using the gates leads gradient formulations to have additive nature which helps them to be more resistant to vanishing and consequently learn long sequences of data. The proposed macromodeling method is capable of modeling nonlinear circuits more accurately and using fewer parameters compared to the conventional LSTM macromodeling method. To further improve the GRU performance, a regularization technique called Gaussian dropout is applied in this paper on deep GRU (GDGRU) to reduce the overfitting problem resulting in better test error. Additionally, the models obtained from the proposed techniques are remarkably faster than the original transistor-level models. To verify the superiority of the proposed method, time-domain modeling of three nonlinear circuits is provided. For these circuits, the comparisons of the accuracy and speed between the conventional recurrent neural network (RNN), the LSTM, and the proposed macromodeling methods are provided.

Prediction and optimisation of fuel consumption for inland ships considering real-time status and environmental factors

This paper proposes a hybrid approach combining Recurrent Neural Network (RNN) and polynomial regression methods for time-domain modeling of nonlinear circuits. The proposed hybrid RNN-polynomial regression (HRPR) method merges RNN and polynomial regression which leads to a significant reduction in training time while providing speedup in simulation compared to both conventional RNN and existing models in simulation tools without sacrificing accuracy. The proposed HRPR method comprises two steps: First, an RNN structure is generated, and then, the output of the RNN is combined with external input(s) of the circuit to perform a regression. Applying this method causes part of the training process to be done by polynomial regression which is simpler than training an RNN. Also, the RNN used in the HRPR method has a simpler structure than a single conventional RNN used for modeling the same component. To verify the validity of the proposed method, modeling and comparisons of three nonlinear examples are presented in this paper.

Successful detection of pedestrians in an autonomous driving scene is challenging because of the high variation in pedestrian scales and the cluttered background which requires context information. In this paper, we propose to use 3-D recurrent neural networks (RNN) to extract rich spatial context information of different resolutions to improve the accuracy of pedestrian detection. Recurrent neural networks have been shown to improve the performance for many tasks like speech recognition by exploiting the dependency between inputs at different time steps. However, the chain structure in conventional RNN is not very suitable for 2-D image tasks. The proposed 3-D RNN has a multi-path structure that enables the context aggregation between not only the neighboring cells but also feature map of different resolutions and abstraction levels. The context extracted in this way is rich because both the spatial dependency on the same feature map and the information across different feature maps are taken into account. By combining the 3-D RNN with skip-pooling, this method can detect multi-scale pedestrians under complex driving scenarios. Performance evaluation shows that the proposed method has a competitive performance, with an average precision of 73.04% on KITTI dataset, and a miss rate of 9.31% on Caltech dataset.

China has made considerable investments in medical resources, in terms of manpower, material, and financial inputs; yet the problems of medical access and medical expenses remain a huge predicament for the average citizen. The ‘efficiency of medical services’ is something that concerns the health-related rights of the entire body of people. For that reason, this study first reviews the theoretical background to the subject from the perspective of the ‘health efficiency theory.’ It uses the Data Envelopment Analysis method to measure the efficiency of both international and domestical medical services. It analyzes the various factors that influence efficiency and then looks at the impact of commercial health insurance on medical efficiency. It then presents policy recommendations that are meant to be practical and feasible.

This paper proposes a feed-forward neural network predictor to adaptively estimate and suppress the narrowband interference (NBI) in the Direct Sequence-Code Division Multiple Access (DS-CDMA) signal. The iterative code-aided estimation is used to further improve the system performance. Simulation results reveal that the proposed algorithm outperforms conventional linear prediction filtering and recurrent neural networks (RNN) based NBI rejection methods, in different interference models.

Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network

Collaboration among healthcare organizations depends on coordination, communication and control among healthcare organizations and effective sharing of medical information and knowledge. Medical services are knowledge-intensive activities. All information, knowledge, techniques and experience should be integrated, managed and shared using the Internet and information technology. Overall medical service quality and efficiency would be improved markedly if medical professionals and staff at different healthcare organizations could use and share medical knowledge resources. Therefore, a collaborative medical knowledge service would promote medical service quality. This study presents a novel medical knowledge service system for cross-organizational healthcare collaboration such that all medical professionals and staff at different healthcare organizations could capture, store, manage, integrate and share medical knowledge. This system should improve medical service quality and efficiency, and promote competition in the healthcare industry. Thus, this study (i) proposes a collaborative medical knowledge service model, (ii) designs a collaborative medical knowledge service system framework, (iii) develops this proposed system, and (iv) evaluates the developed system based on user satisfaction.

Prediction concept needs human-like thinking and ideas. ANN helps make a program for describing the solutions to the prediction problems. To gain accurate prediction, there is a need for deep thinking in the knowledge system discovered. Deep learning helps form such next-level type of ANN thinking. Deep learning models do not require humans for programming the problems. It can determine the prediction by itself by learning the dataset. This paper describes the prediction by considering sequential model for continuous accessing of data with the help of multi-layers. Deep learning is very helpful for predicting larger dataset. This paper proposes an algorithmic model known as a Comprehensive Deep Recurrent Artificial Neural Networks (CDRANN) by hybridizing LSTM (long short-term memory) with RNN (recurrent neural network) backtrack solver for better future stock prediction. This paper also explains the need for hybridizing evolutionary model CDRANN to create a novel change in future prediction.KeywordsCDRANN (Comprehensive Deep Recurrent Artificial Neural Networks)LSTM (long short-term memory)RNN (recurrent neural network)Stock marketPrediction