Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport.

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

Big data ordination towards intensive care event count cases using fast computing GLLVMS.

BACKGROUND: In heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high dimensional data. METHODS: In this paper, we perform high dimensional ordination towards event counts in intensive care hospital for Emergency Department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM's). RESULTS: During the analysis, we measure the performance and calculate the time computing of GLLVM by employing variational approximation and Laplace approximation, and compare the different distributions, including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedie, respectively. GLLVMs (Generalized Linear Latent Variable Models), an extended version of GLMs (Generalized Linear Models) with latent variables, have fast computing time. The major challenge in latent variable modelling is that the function [Formula: see text] is not trivial to solve since the marginal likelihood involves integration over the latent variable u. CONCLUSIONS: In a nutshell, GLLVMs lead as the best performance reaching the variance of 98% comparing other methods. We get the best model negative binomial and Variational approximation, which provides the best accuracy by accuracy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.

Real-Time Facial Affective Computing on Mobile Devices.

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.

Construction of a clinical decision support system for undergoing surgery based on domain ontology and rules reasoning.

OBJECTIVE: To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. MATERIALS AND METHODS: The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé-Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. RESULTS: The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. CONCLUSIONS: The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia.

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks.

Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations and extracts the vertical distance between the eyelids using the facial landmark positions. Our results show that the recognizable landmarks are sufficiently accurate to determine the degree of eye-opening and closing consistently. The proposed algorithm estimates the facial landmark positions, extracts a single scalar quantity by using Modified Eye Aspect Ratio (Modified EAR) and characterizing the eye closeness in each frame. Finally, blinks are detected by the Modified EAR threshold value and detecting eye blinks as a pattern of EAR values in a short temporal window. According to the results from a typical data set, it is seen that the suggested approach is more efficient than the state-of-the-art technique.

NTMG (N-terminal Truncated Mutants Generator for cDNA): an automatic multiplex PCR assays design for generating various N-terminal truncated cDNA mutants.

The sequential deletion method is generally used to locate the functional domain of a protein. With this method, in order to find the various N-terminal truncated mutants, researchers have to investigate the ATG-like codons, to design various multiplex polymerase chain reaction (PCR) forward primers and to do several PCR experiments. This web server (N-terminal Truncated Mutants Generator for cDNA) will automatically generate groups of forward PCR primers and the corresponding reverse PCR primers that can be used in a single batch of a multiplex PCR experiment to extract the various N-terminal truncated mutants. This saves much time and money for those who use the sequential deletion method in their research. This server is available at http://oblab.cs.nchu.edu.tw:8080/WebSDL/.

Robust Dimension Reduction for Clustering With Local Adaptive Learning.

In pattern recognition and data mining, clustering is a classical technique to group matters of interest and has been widely employed to numerous applications. Among various clustering algorithms, K-means (KM) clustering is most popular for its simplicity and efficiency. However, with the rapid development of the social network, high-dimensional data are frequently generated, which poses a considerable challenge to the traditional KM clustering as the curse of dimensionality. In such scenarios, it is difficult to directly cluster such high-dimensional data that always contain redundant features and noises. Although the existing approaches try to solve this problem using joint subspace learning and KM clustering, there are still the following limitations: 1) the discriminative information in low-dimensional subspace is not well captured; 2) the intrinsic geometric information is seldom considered; and 3) the optimizing procedure of a discrete cluster indicator matrix is vulnerable to noises. In this paper, we propose a novel clustering model to cope with the above-mentioned challenges. Within the proposed model, discriminative information is adaptively explored by unifying local adaptive subspace learning and KM clustering. We extend the proposed model using a robust l2,1 -norm loss function, where the robust cluster centroid can be calculated in a weighted iterative procedure. We also explore and discuss the relationships between the proposed algorithm and several related studies. Extensive experiments on kinds of benchmark data sets demonstrate the advantage of the proposed model compared with the state-of-the-art clustering approaches.

Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis.

Introduction: Although a number of researchers have considered the positive potential of Clinical Decision Support System (CDSS), they did not consider that patients' attitude which leads to active treatment strategies or HbA1c targets. Materials and Methods: We adopted the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) published to propose an HbA1c target and antidiabetic medication recommendation system for patients. Based on the antidiabetic medication profiles, which were presented by the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE), we use TOPSIS to calculate the ranking of antidiabetic medications. Results: The endocrinologist set up ten virtual patients' medical data to evaluate a decision support system. The system indicates that the CDSS performs well and is useful to 87%, and the recommendation system is suitable for outpatients. The evaluation results of the antidiabetic medications show that the system has 85% satisfaction degree which can assist clinicians to manage T2DM while selecting antidiabetic medications. Conclusions: In addition to aiding doctors' clinical diagnosis, the system not only can serve as a guide for specialty physicians but also can help nonspecialty doctors and young doctors with their drug prescriptions.

Design of multiplex PCR primers using heuristic algorithm for sequential deletion applications.

UNLABELLED: The sequential deletion method is commonly applied to locate the functional domain of a protein. Unfortunately, manually designing primers for multiplex polymerase chain reaction (PCR) is a labor-intensive task. In order to speed up the experimental procedure and to improve the efficiency of producing PCR products, this paper proposes a multiplex PCR primers (MPCRPs) designer to design multiple forward primers with a single 3'-UTR reverse primer for extracting various N-terminal truncated mutants to quickly locate the functional domain of a cDNA sequence. Several factors, including melting temperature, primer length, GC content, internal self-complement, cross-dimerization, terminal limitation, and specificity, are used as the criteria for designing primers. This study obtains a near-optimal solution of primer sets that can be placed in as few test tubes as possible for one multiplex PCR experiment. RESULTS: Homo sapiens ribosomal protein L5, Homo sapiens xylosyltransferase I, and Bacteriophage T4 gene product 11 were used as test examples to verify efficacy of the proposed algorithm. In addition, the designed primers of Homo sapiens ribosomal protein L5 cDNA were applied in multiplex PCR experiments. A total of 48 forward primers and one reverse primer were designed and used to duplicate N-terminal truncated mutants of different lengths from the protein. The primers were classified into eight tube groups (i.e., test tubes) held within the same temperature range (53-57 degrees C), and the validity of the PCR products were verified using polyacrylamide gel electrophoresis (PAGE) with the functional domain correctly located. A software implementation of the proposed algorithm useful in assisting the researcher to design primers for multiplex PCR experiments was developed and available upon request.