Establishment-level safety analytics: a scoping review

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

Competitive performance as a discriminator of doping status in elite athletes

As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. This research study aimed to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti-doping rule violation (ADRV) and those who do not. We analysed performances of male and female 100 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age-matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78-0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for anti-doping purposes.

What should next-generation analytical platforms for biopharmaceutical production look like?

Biotherapeutic products, particularly complex products such as monoclonal antibodies (mAbs), have as many as 20-30 critical quality attributes (CQAs), thereby requiring a collection of orthogonal, high-resolution analytical tools for characterization and making characterization a resource-intensive task. As discussed in this Opinion, the need to reduce the cost of developing biotherapeutic products and the need to adopt Industry 4.0 and eventually Industry 5.0 paradigms are driving a reappraisal of existing analytical platforms. Next-generation platforms will have reduced offline testing, renewed focus on online testing and real-time monitoring, multiattribute monitoring, and extensive use of advanced data analytics and automation. They will be more complex, more sensitive, resource lean, and more responsive compared with existing platforms.

Identifying potential predictable indicators for the management of tertiary hospitals

BACKGROUND

The European University Hospitals Alliance (EUHA) recognises the need to move from the classical approach of measuring key performance indicators (KPIs) to an anticipative approach based on predictable indicators to take decisions (Key Decision Indicators, KDIs). It might help managers to anticipate poor results before they occur to prevent or correct them early.

OBJECTIVE

This paper aims to identify potential KDIs and to prioritize those most relevant for high complexity hospitals.

METHODS

A narrative review was performed to identify KPIs with the potential to become KDIs. Then, two surveys were conducted with EUHA hospital managers (n = 51) to assess potential KDIs according to their relevance for decision-making (Value) and their availability and effort required to be predicted (Feasibility). Potential KDIs are prioritized for testing as predictable indicators and developing in the short term if they were classified as highly Value and Feasible.

RESULTS

The narrative review identified 45 potential KDIs out of 153 indicators and 11 were prioritized. Of nine EUHA hospitals, 25 members from seven answered, prioritizing KDIs related to the emergency department (ED), hospitalisation and surgical processes (n = 8), infrastructure and resources (n = 2) and health outcomes and quality (n = 1). The highest scores in this group were for those related to ED. The results were homogeneous among the different hospitals.

CONCLUSIONS

Potential KDIs related to care processes and hospital patient flow was the most prioritized ones to test as being predictable. KDIs represent a new approach to decision-making, whose potential to be predicted could impact the planning and management of hospital resources and, therefore, healthcare quality.

Racing the Machine: Data Analytic Technologies and Institutional Inscription of Racialized Health Injustice

Recent scientific and policy initiatives frame clinical settings as sites for intervening upon inequality. Electronic health records and data analytic technologies offer opportunity to record standard data on education, employment, social support, and race-ethnicity, and numerous audiences expect biomedicine to redress social determinants based on newly available data. However, little is known on how health practitioners and institutional actors view data standardization in relation to inequity. This article examines a public safety-net health system's expansion of race, ethnicity, and language data collection, drawing on 10 months of ethnographic fieldwork and 32 qualitative interviews with providers, clinic staff, data scientists, and administrators. Findings suggest that electronic data capture institutes a decontextualized racialization within biomedicine as health practitioners and data workers rely on biological, cultural, and social justifications for collecting racial data. This demonstrates a critical paradox of stratified biomedicalization: The same data-centered interventions expected to redress injustice may ultimately reinscribe it.

Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study

BACKGROUND

The phenomenon of patients missing booked appointments without canceling them-known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)-has a detrimental effect on patients' health and results in massive health care resource wastage.

OBJECTIVE

Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand.

METHODS

We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms' prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics.

RESULTS

Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients' DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction.

CONCLUSIONS

This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.

Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1

BACKGROUND

The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency.

OBJECTIVE

This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition.

METHODS

The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection.

RESULTS

A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository.

CONCLUSION

The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.

Weighted Domain Adaptation Using the Graph-Structured Dataset Representation for Machinery Fault Diagnosis under Varying Operating Conditions

Data-driven fault diagnosis has received significant attention in the era of big data. Most data-driven methods have been developed under the assumption that both training and test data come from identical data distributions. However, in real-world industrial scenarios, data distribution often changes due to varying operating conditions, leading to a degradation of diagnostic performance. Although several domain adaptation methods have shown their feasibility, existing methods have overlooked metadata from the manufacturing process and treated all domains uniformly. To address these limitations, this article proposes a weighted domain adaptation method using a graph-structured dataset representation. Our framework involves encoding a collection of datasets into the proposed graph structure, which captures relations between datasets based on metadata and raw data simultaneously. Then, transferability scores of candidate source datasets for a target are estimated using the constructed graph and a graph embedding model. Finally, the fault diagnosis model is established with a voting ensemble of the base classifiers trained on candidate source datasets and their estimated transferability scores. For validation, two case studies on rotor machinery, specifically tool wear and bearing fault detection, were conducted. The experimental results demonstrate the effectiveness and superiority of the proposed method over other existing domain adaptation methods.

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Background

Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices.

Objective

This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors.

Methods

People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors.

Results

A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007).

Conclusions

This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

ChatGPT and Artificial Intelligence in Hospital Level Research: Potential, Precautions, and Prospects

Rapid advancements in artificial intelligence (AI) have revolutionized numerous sectors, including medical research. Among the various AI tools, OpenAI's ChatGPT, a state-of-the-art language model, has demonstrated immense potential in aiding and enhancing research processes. This review explores the application of ChatGPT in medical hospital level research, focusing on its capabilities for academic writing assistance, data analytics, statistics handling, and code generation. Notably, it delves into the model's ability to streamline tasks, support decision making, and improve patient interaction. However, the article also underscores the importance of exercising caution while dealing with sensitive healthcare data and highlights the limitations of ChatGPT, such as its potential for erroneous outputs and biases. Furthermore, the review discusses the ethical considerations that arise with AI use in health care, including data privacy, AI interpretability, and the risk of AI-induced disparities. The article culminates by envisioning the future of AI in medical research, emphasizing the need for robust regulatory frameworks and guidelines that balance the potential of AI with ethical considerations. As AI continues to evolve, it holds promising potential to augment medical research in a manner that is ethical, equitable, and patient-centric.

From Microcosm to Macrocosm: The -Omics, Multiomics, and Sportomics Approaches in Exercise and Sports

This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of n-of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies.

Effects of Virtual Reality Pilates Training on Duration of Posture Maintenance and Flow in Young, Healthy Individuals: Randomized Crossover Trial

BACKGROUND

This study explored the use of virtual reality (VR) technology to enhance the effectiveness and duration of low-intensity movements and postures in Pilates-derived exercises. We postulate that by leveraging the flow state in VR, individuals can engage in these exercises for longer periods while maintaining a high level of flow.

OBJECTIVE

The purpose of this study was to compare differences in posture maintenance and flow between VR Pilates training and conventional Pilates training, and the correlation between the 2 factors.

METHODS

The 18 participants in each group received either VR training or conventional training and were switched to the other training type after a 2-day wash-out period. Each group performed Pilates movements in a VR environment and a conventional environment, divided into 4 types. After training sessions, participants were evaluated for flow using a self-report questionnaire. In addition, a sports video analysis program was used to measure the duration of posture maintenance in 2 video-recorded sessions. Repeated-measures ANOVA and correlation analysis were performed on the measured duration of posture maintenance and flow scores. In all cases, the statistical significance level was set at P<.05.

RESULTS

Results for the duration of posture maintenance verification by type showed that simple behavior (F1,16=17.631; P<.001), upper body-arm coordination behavior (F1,16=6.083; P=.04), upper body-leg coordination behavior (F1,16=8.359; P<.001), and whole-body coordination behavior (F1,16=8.426; P<.001) all showed an interaction effect at P<.05. Flow (F1,16=15.250; P<.001) also showed an interaction effect. In addition, significant correlations were determined between duration of all types of posture maintenance and flow in the VR training group at P<.05.

CONCLUSIONS

Our results indicate that VR Pilates training may be more useful than conventional Pilates training in improving the duration of posture maintenance and that it promotes a significantly higher degree of flow when compared with conventional Pilates training.

Healthcare analytics-A literature review and proposed research agenda

This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field.

SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform

Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and magnetic susceptibility data for healthy and diseased conditions, acquired using MR spectroscopy (MRS) and quantitative susceptibility mapping (QSM) respectively. The SWADESH architecture also provides a neuroimaging database which includes MRI, MRS, functional MRI (fMRI), diffusion weighted imaging (DWI), QSM, neuropsychological data and associated data analysis pipelines. Our final objective is to provide a master database of major brain disease states (neurodegenerative, neuropsychiatric, neurodevelopmental, and others) and to identify characteristic features and biomarkers associated with such disorders.

Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives

Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives. This report utilized a publicly available machine-learning data package in R (MLDataR) and computational packages (XGBoost) to highlight techniques for machine-learning model development and visualization with SHaply Additive exPlanations (SHAP). A simple six-step process along with example code was constructed to build and visualize machine-learning models. A concrete set of three steps was developed to help with interpretation. Further teaching of these methods could benefit researchers by providing alternative methods for data analysis in medical studies. These could help researchers without computational experience to get a feel for machine learning to better understand the literature and technique.

Recent Advances in High-Content Imaging and Analysis in iPSC-Based Modelling of Neurodegenerative Diseases

In the field of neurodegenerative pathologies, the platforms for disease modelling based on patient-derived induced pluripotent stem cells (iPSCs) represent a valuable molecular diagnostic/prognostic tool. Indeed, they paved the way for the in vitro recapitulation of the pathological mechanisms underlying neurodegeneration and for characterizing the molecular heterogeneity of disease manifestations, also enabling drug screening approaches for new therapeutic candidates. A major challenge is related to the choice and optimization of the morpho-functional study designs in human iPSC-derived neurons to deeply detail the cell phenotypes as markers of neurodegeneration. In recent years, the specific combination of high-throughput screening with subcellular resolution microscopy for cell-based high-content imaging (HCI) screening allowed in-depth analyses of cell morphology and neurite trafficking in iPSC-derived neuronal cells by using specific cutting-edge microscopes and automated computational assays. The present work aims to describe the main recent protocols and advances achieved with the HCI analysis in iPSC-based modelling of neurodegenerative diseases, highlighting technical and bioinformatics tips and tricks for further uses and research. To this end, microscopy requirements and the latest computational pipelines to analyze imaging data will be explored, while also providing an overview of the available open-source high-throughput automated platforms.

Overview of methods and available tools used in complex brain disorders

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually and in combination, increasing mortality risk, and contributing to the socioeconomic burden of patients, their families and, their communities at large. Although some literature reviews have been conducted mentioning the available methods and tools used for supporting the diagnosis of complex brain disorders and processing different files, there are still limitations. Specifically, these research works have focused primarily on one single brain disorder, i.e., sleep disorders or dementia or epilepsy. Additionally, existing research initiatives mentioning some tools, focus mainly on one single type of data, i.e., electroencephalography (EEG) signals or actigraphies or Magnetic Resonance Imaging, and so on. To tackle the aforementioned limitations, this is the first study conducting a comprehensive literature review of the available methods used for supporting the diagnosis of multiple complex brain disorders, i.e., Alzheimer's dementia, sleep disorders, epilepsy. Also, to the best of our knowledge, we present the first study conducting a comprehensive literature review of all the available tools, which can be exploited for processing multiple types of data, including EEG, actigraphies, and MRIs, and receiving valuable forms of information which can be used for differentiating people in a healthy control group and patients suffering from complex brain disorders. Additionally, the present study highlights both the benefits and limitations of the existing available tools.

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

A scoping review of the clinical application of machine learning in data-driven population segmentation analysis

OBJECTIVE

Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations.

MATERIALS AND METHODS

MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included.

RESULTS

We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population.

DISCUSSION

Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling.

CONCLUSION

Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.

Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning

The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called "spaghetti-like" process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case.

Data Analytics of Broiler Growth Dynamics and Feed Conversion Ratio of Broilers Raised to 35 d under Commercial Tropical Conditions

Data collection is standard in commercial broiler production; however, growth modeling is still a challenge since this data often lacks an inflection point. This study evaluated body weight (BW) dynamics, feed intake, BW gain, feed conversion ratio (FCR), and mortality of broiler flocks reared under commercial tropical conditions with controlled feeding to optimize FCR. The data analyzed included performance records of 1347 male and 1353 female Ross 308 AP broiler flocks with a total of 95.4 million chickens housed from 2018 to 2020. Decision trees determined high- and low-feed-efficiency groups using FCR at 35 d. Logistic, Gompertz-Laird, and von Bertalanffy growth models were fitted with weekly BW data for each flock within performance groups. The logistic model indicated more accurate estimates with biological meaning. The high-efficiency males and females (p < 0.001) were offered less feed than the low-efficiency group and were consistently more efficient. In conclusion, greater feeding control between the second and the fourth week of age, followed by higher feed allowance during the last week, was associated with better feed efficiency at 35 d in males and females. Additionally, models demonstrated that a reduced growth rate resulted in heavier chickens at 35 d with better feed efficiency and greater BW gain.

Studying infant mortality: A demographic analysis based on data mining models

Child mortality, particularly among infants below 5 years, is a significant community well-being concern worldwide. The health sector's top priority in emerging states is to minimize children's death and enhance infant health. Despite a substantial decrease in worldwide deaths of children below 5 years, it remains a significant community well-being concern. Children under five years of age died at 37 per 1,000 live birth globally in 2020. However, in underdeveloped countries such as Pakistan and Ethiopia, the fatality rate of children per 1,000 live birth is 65.2 and 48.7, respectively, making it challenging to reduce. Predictive analytics approaches have become well-known for predicting future trends based on previous data and extracting meaningful patterns and connections between parameters in the healthcare industry. As a result, the objective of this study was to use data mining techniques to categorize and highlight the important causes of infant death. Datasets from the Pakistan Demographic Health Survey and the Ethiopian Demographic Health Survey revealed key characteristics in terms of factors that influence child mortality. A total of 12,654 and 12,869 records from both datasets were examined using the Bayesian network, tree (J-48), rule induction (PART), random forest, and multi-level perceptron techniques. On both datasets, various techniques were evaluated with the aforementioned classifiers. The best average accuracy of 97.8% was achieved by the best model, which forecasts the frequency of child deaths. This model can therefore estimate the mortality rates of children under five years in Ethiopia and Pakistan. Therefore, an online model to forecast child death based on our research is urgently needed and will be a useful intervention in healthcare.

Nanosensor technologies and the digital transformation of healthcare

Nanosensors are nanoscale devices that measure physical attributes and convert these signals into analyzable information. In preparation, for the impending reality of nanosensors in clinical practice, we confront important questions regarding the evidence supporting widespread device use. Our objectives are to demonstrate the value and implications for new nanosensors as they relate to the next phase of remote patient monitoring and to apply lessons learned from digital health devices through real-world examples.

Pac-12 Health Analytics Program: An Innovative Approach to Health Care Operations, Data Analytics, and Clinical Research in Intercollegiate Athletics

The objective of this study was to describe the purpose, methods, and effects of the Pac-12 Health Analytics Program (HAP) approach on sports medicine informatics, research, analytics, and health care operations. Sports injury-surveillance initiatives have been supporting the clinical research community in sports medicine for nearly 4 decades. Whereas the initial systems tracked only a few sports, current surveillance programs have expanded to include entire professional and elite athlete organizations, providing important statistics on sports injury risk management. The HAP is a conference-wide data-sharing and-analytics program. It collects authorized, deidentified clinical data, encompassing multiple domains of sports medicine injury management, including sports injuries and illnesses, concussions, risk exposure, and COVID-19 testing elements. The HAP provides clinicians with access to curated data to inform evidence-based practice and support local health care operations with respect to emerging sports injury trends. The HAP supplies approved research groups with access to a data repository that describes a homogeneous, elite intercollegiate athlete sample, thereby supporting nonresearch clinical initiatives as well as contributions to peer-reviewed research that can improve the health and well-being of Pac-12 student-athletes. The HAP is a novel approach to sports injury epidemiology and surveillance that has allowed the Pac-12 Conference to meet larger objectives regarding improving the student-athlete experience and clinical research among its member schools. Data quality control has improved the accuracy of the data and value to clinical athletic trainers within the conference. Curated dashboards displaying aggregated project data offer clinicians data-driven decision-making tools that help inform sports injury risk management. As of 2021, the HAP had supported more than 3 dozen data requests. These investigations have resulted in numerous peer-reviewed research contributions to the sports medicine community with findings that have great potential to improve the health and well-being of Pac-12 student-athletes.

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.

Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data

In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems, namely patient registration and laboratory information systems. These workflows exploit various FHIR Application programming interface (APIs) to facilitate patient-centered and cohort-based interactive analyses. We developed an FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework's ability to generate various analytics from clinical data represented in the FHIR resources.

Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics

Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD ₄, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MI _CL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD ₄ takes into account the available metadata and summaries all relevant statistics generated in steps 1-3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD ₄ methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD ₄ identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD ₄ embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making.

The Use of Machine Learning to Predict Late Arrivals at the Adult Outpatient Department

INTRODUCTION

Patient unpunctuality leads to delays in the delivery of care and increased waiting times, resulting in crowdedness. Late arrivals for adult outpatient appointments are a challenge for healthcare, contributing to negative effects on the efficiency of health services as well as wasted time, budget, and resources. This study aims to identify factors and characteristics associated with tardy arrivals at adult outpatient appointments using machine learning and artificial intelligence. The goal is to create a predictive model using machine learning models capable of predicting adult patients arriving late to their appointments. This would support effective and accurate decision-making in scheduling systems, leading to better utilization and optimization of healthcare resources.

METHODS

A retrospective cohort review of adult outpatient appointments between January 1, 2019, and December 31, 2019, was undertaken at a tertiary hospital in Riyadh. Four machine learning models were used to identify the best prediction model that could predict late-arriving patients based on multiple factors.

RESULTS

A total of 1,089,943 appointments for 342,974 patients were conducted. There were 128,121 visits (11.7%) categorized as late arrivals. The best prediction model was Random Forest, with a high accuracy of 94.88%, a recall of 99.72%, and a precision of 90.92%. The other models showed different results, such as XGBoost with an accuracy of 68.13%, Logistic Regression with an accuracy of 56.23%, and GBoosting with an accuracy of 68.24%.

CONCLUSION

This paper aims to identify the factors associated with late-arriving patients and improve resource utilization and care delivery. Despite the overall good performance of the machine learning models developed in this study, not all variables and factors included contribute significantly to the algorithms' performance. Considering additional variables could improve machine learning performance outcomes, further enhancing the practical application of the predictive model in healthcare settings.

Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods

The global economy has suffered losses as a result of the COVID-19 epidemic. Accurate and effective predictive models are necessary for the governance and readiness of the healthcare system and its resources and, ultimately, for the prevention of the spread of illness. The primary objective of the project is to build a robust, universal method for predicting COVID-19-positive cases. Collaborators will benefit from this while developing and revising their pandemic response plans. For accurate prediction of the spread of COVID-19, the research recommends an adaptive gradient LSTM model (AGLSTM) using multivariate time series data. RNN, LSTM, LASSO regression, Ada-Boost, Light Gradient Boosting and KNN models are also used in the research, which accurately and reliably predict the course of this unpleasant disease. The proposed technique is evaluated under two different experimental conditions. The former uses case studies from India to validate the methodology, while the latter uses data fusion and transfer-learning techniques to reuse data and models to predict the onset of COVID-19. The model extracts important advanced features that influence the COVID-19 cases using a convolutional neural network and predicts the cases using adaptive LSTM after CNN processes the data. The experiment results show that the output of AGLSTM outperforms with an accuracy of 99.81% and requires only a short time for training and prediction.

Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring

Innovative technological solutions are required to improve patients' quality of life and deliver suitable treatment. Healthcare workers may be able to watch patients from a distance using the Internet of Things (IoT) by using big data algorithms to analyze instrument outputs. Therefore, it is essential to gather information on use and health problems in order to improve the remedies. To ensure seamless incorporation for use in healthcare institutions, senior communities, or private homes, these technological tools must first and foremost be easy to use and implement. We provide a network cluster-based system known as smart patient room usage in order to achieve this. As a result, nursing staff or caretakers can use it efficiently and swiftly. This work focuses on the exterior unit that makes up a network cluster, a cloud storage mechanism for data processing and storage, as well as a wireless or unique radio frequency send module for data transfer. In this article, a spatio-temporal cluster mapping system is presented and described. This system creates time series data using sense data collected from various clusters. The suggested method is the ideal tool to use in a variety of circumstances to improve medical and healthcare services. The suggested model's ability to anticipate moving behavior with high precision is its most important feature. The time series graphic displays a regular light movement that continued almost the entire night. The last 12 h' lowest and highest moving duration numbers were roughly 40% and 50%, respectively. When there is little movement, the model assumes a normal posture. Particularly, the moving duration ranges from 7% to 14%, with an average of 7.0%.

ASHP National Survey of Pharmacy Practice in Hospital Settings: Workforce - 2022

DISCLAIMER

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE

Results of the 2022 ASHP National Survey of Pharmacy Practice in Hospital Settings are presented.

METHODS

Pharmacy directors at 1,498 general and children's medical/surgical hospitals in the United States were surveyed using a mixed-mode method of contact by email and mail. Survey completion was online. IQVIA supplied data on hospital characteristics; the survey sample was drawn from IQVIA's hospital database.

RESULTS

The response rate was 23.7%. Inpatient pharmacists independently prescribe in 27.1% of hospitals. Advanced analytics are used in 8.7% of hospitals. Pharmacists work in ambulatory or primary care clinics in 51.6% of hospitals operating outpatient clinics. Some level of pharmacy service integration is reported in 53.6% of hospitals. More advanced pharmacy technician roles are emerging. For health systems offering hospital at home services, 65.9% of pharmacy departments are involved. Shortages of pharmacists and technicians were reported but are more acute for pharmacy technicians. Aspects of burnout are being measured in 34.0% of hospitals, and 83.7% are attempting to prevent and mitigate burnout. The average number of full-time equivalents per 100 occupied beds is 16.9 for pharmacists and 16.1 for pharmacy technicians.

CONCLUSION

Health-system pharmacies are experiencing workforce shortages; however, these shortages have had limited impact on budgeted positions. Workforce challenges are influencing the work of pharmacists and pharmacy technicians. Adoption of practice advancement initiatives has continued the positive trend from past years despite workforce issues.

Identifying Hospital Deserts in Texas Before and During the COVID-19 Outbreak

In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers.

Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability

In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to this, the main work emphasizes observing multiple parameters of the In-Vehicle system such as speed, distance covered, idle time, and fuel economy by the electronic control unit are evaluated in this process. Electronic control unit through brake control module, powertrain control module, transmission control module, suspension control module, and battery management system helps to predict the nature of drive-in different terrains and also can suggest effective custom driving modes for advanced driver assistance systems. These features are implemented with the help of the vehicle-to-infrastructure protocol, which collects data through gateway nodes that can be visualized in the IoT data frame. The proposed work involves the process of analyzing and visualizing the driver-influencing factors of a modern vehicle that is in connection with the IoT cloud platform. The custom drive mode suggestion and improvisation had been completed with help of computational analytics that leads to the deployment of an over-the-air update to the vehicle embedded system upgradation for betterment in drivability. These operations are progressed through a cloud server which is the prime factor proposed in this work.

Using data analytics for telehealth utilization: A case study in Arkansas

INTRODUCTION

Many patients used telehealth services during the COVID-19 pandemic. In this study, we evaluate how different factors have affected telehealth utilization in recent years. Decision makers at the federal and state levels can use the results of this study to inform their healthcare-related policy decisions.

METHODS

We implemented data analytics techniques to determine the factors that explain the use of telehealth by developing a case study using data from Arkansas. Specifically, we built a random forest regression model which helps us identify the important factors in telehealth utilization. We evaluated how each factor impacts the number of telehealth patients in Arkansas counties.

RESULTS

Of the 11 factors evaluated, five are demographic, and six are socioeconomic factors. Socioeconomic factors are relatively easier to influence in the short term. Based on our results, broadband subscription is the most important socioeconomic factor and population density is the most important demographic factor. These two factors were followed by education level, computer use, and disability in terms of their importance as it relates to telehealth use.

DISCUSSION

Based on studies in the literature, telehealth has the potential to improve healthcare services by improving doctor utilization, reducing direct and indirect waiting times, and reducing costs. Thus, federal and state decision makers can influence the utilization of telehealth in specific locations by focusing on important factors. For example, investments can be made to increase broadband subscriptions, education levels, and computer use in targeted locations.

A Framework for Developing Health Equity Initiatives in Radiology

PURPOSE

In recent years, radiology departments have increasingly recognized the extent of health care disparities related to imaging and image-guided interventions. The goal of this article is to provide a framework for developing a health equity initiative in radiology and to articulate key defining factors.

METHODS

This article leverages the experience of three academic radiology departments and explores key principles that emerged when observing the experiences of these departments that have begun to engage in health equity-focused work.

RESULTS

A four-component framework is described for a health equity initiative in radiology consisting of (1) environmental scan and blueprint, (2) design and implementation, (3) initiative evaluation, and (4) community engagement. Key facilitators include a comprehensive environmental scan, early stakeholder engagement and consensus building, implementation science design thinking, and multitiered community engagement.

CONCLUSIONS

All radiology organizations should strive to develop, pilot, and evaluate novel initiatives that promote equitable access to high-quality imaging services. Establishing systems for high-quality data collection is critical to success. An implementation science approach provides a robust framework for developing and testing novel health equity initiatives in radiology. Community engagement is critical at all stages of the health equity initiative time line.

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

Implementation model of data analytics as a tool for improving internal audit processes

Introduction

The aim of this article is to understand the importance of internal audit departments todays-part of corporate governance and guardian of the organisation's culture and climate-, as well as the opportunities that new technologies offer to increase their effectiveness and efficiency.

Methods

To this end, based on an exhaustive review of the literature, the concepts of internal audit and data analytics are related, and a framework is proposed for the implementation of a technology of these characteristics in an internal audit department.

Results

The results of the research show that those companies that invest resources in readapting their processes to technological change are likely to obtain better results than those organisations that keep their management procedures obsolete.

Discussion

Based on these results, it is concluded that there is a need to consider technological change in internal audit departments, specifically data analytics, to increase the effectiveness and efficiency of audit processes.

Advances in bioreactor control for production of biotherapeutic products

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.

Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS

The need to achieve digital gas sensing technology, namely, a technology to sense and transmit gas-enabled digital media, has been recognized as highly challenging. This challenge has motivated the authors to focus on complementary metal oxide semiconductor silicon on insulator micro electro-mechanical system (CMOS-SOI-MEMS) technologies, and the result is a new pellistor-like sensor, dubbed GMOS, with integrated signal processing. In this study, we describe the performance of such sensors for the selective detection of mixtures of gases. The novel key ideas of this study are: (i) the use of the GMOS for gas sensing; (ii) applying the Kalman filter to improve the signal-to-noise ratio; (iii) adding artificial intelligence (AI) with tiny edge approach.

Competitive intelligence: A precursor to a learning health system

Unlike other developed countries, the US healthcare system is largely privatized and highly competitive. This dynamic stifles effective information sharing, while the need for prompt and accurate evidence-based decision making has become crucial. Crises, like the COVID-19 pandemic, elevate the importance of quality decision making and exacerbate issues associated with the lack of a cohesive system to share information. Competitive intelligence (CI) is a discipline that encourages gathering, analyzing, and sharing information throughout a firm in order to develop and sustain competitive advantage. CI could be considered a precursor in establishing a learning organization (LO). Although CI research has focused on its process and value, little is found in the literature on how to integrate CI into an organization; this is particularly true in healthcare. A conceptual model is proposed to build and integrate a CI function and culture within a healthcare organization to encourage effective information sharing and knowledge development. In turn, this can provide a mechanism to create a learning health system (LHS). Although the model was developed specifically for US healthcare, it offers application to healthcare in other countries as well as most any industry.

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.

Moving beyond velocity derivatives; using global positioning system data to extract sequential movement patterns at different levels of rugby league match-play

This study aims to (a) quantify the movement patterns during rugby league match-play and (b) identify if differences exist by levels of competition within the movement patterns and units through the sequential movement pattern (SMP) algorithm. Global Positioning System data were analysed from three competition levels; four Super League regular (regular-SL), three Super League (semi-)Finals (final-SL) and four international rugby league (international) matches. The SMP framework extracted movement pattern data for each athlete within the dataset. Between competition levels, differences were analysed using linear discriminant analysis (LDA). Movement patterns were decomposed into their composite movement units; then Kruskal-Wallis rank-sum and Dunn post-hoc were used to show differences. The SMP algorithm found 121 movement patterns comprised mainly of "walk" and "jog" based movement units. The LDA had an accuracy score of 0.81, showing good separation between competition levels. Linear discriminant 1 and 2 explained 86% and 14% of the variance. The Kruskal-Wallis found differences between competition levels for 9 of 17 movement units. Differences were primarily present between regular-SL and international with other combinations showing less differences. Movement units which showed significant differences between competition levels were mainly composed of low velocities with mixed acceleration and turning angles. The SMP algorithm found 121 movement patterns across all levels of rugby league match-play, of which, 9 were found to show significant differences between competition levels. Of these nine, all showed significant differences present between international and domestic, whereas only four found differences present within the domestic levels. This study shows the SMP algorithm can be used to differentiate between levels of rugby league and that higher levels of competition may have greater velocity demands.Highlights This study shows that movement patterns and movement units can be used to investigate team sports through the application of the SMP frameworkOne hundred and twenty-one movement patterns were found to be present within rugby league match-play, with the walk- and jog-based movement units most prevalent. No movement pattern was unique to a single competition level.Further analysis revealed that the majority of movement units analysed had significant differences between international and domestic rugby league, whereas only four movement units (i.e. f,m,n,q) had significant differences within the two domestic rugby league levels.International rugby league had higher occurrences of the movement patterns consisting of higher velocity movement units (ie. T,S,y). This suggests that international rugby league players may need greater high velocity exposure in training.

Energy Contour Forecasting Optimization with Smart Metering in Distribution Power Networks

Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity-industrial or commercial-and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted.

Application of a novel index for understanding vascular health following pharmacological intervention in a pre-clinical model of metabolic disease

While a thorough understanding of microvascular function in health and how it becomes compromised with progression of disease risk is critical for developing effective therapeutic interventions, our ability to accurately assess the beneficial impact of pharmacological interventions to improve outcomes is vital. Here we introduce a novel Vascular Health Index (VHI) that allows for simultaneous assessment of changes to vascular reactivity/endothelial function, vascular wall mechanics and microvessel density within cerebral and skeletal muscle vascular networks with progression of metabolic disease in obese Zucker rats (OZR); under control conditions and following pharmacological interventions of clinical relevance. Outcomes are compared to "healthy" conditions in lean Zucker rats. We detail the calculation of vascular health index, full assessments of validity, and describe progressive changes to vascular health index over the development of metabolic disease in obese Zucker rats. Further, we detail the improvement to cerebral and skeletal muscle vascular health index following chronic treatment of obese Zucker rats with anti-hypertensive (15%-52% for skeletal muscle vascular health index; 12%-48% for cerebral vascular health index; p < 0.05 for both), anti-dyslipidemic (13%-48% for skeletal muscle vascular health index; p < 0.05), anti-diabetic (12%-32% for cerebral vascular health index; p < 0.05) and anti-oxidant/inflammation (41%-64% for skeletal muscle vascular health index; 29%-42% for cerebral vascular health index; p < 0.05 for both) drugs. The results present the effectiveness of mechanistically diverse interventions to improve cerebral or skeletal muscle vascular health index in obese Zucker rats and provide insight into the superiority of some pharmacological agents despite similar effectiveness in terms of impact on intended targets. In addition, we demonstrate the utility of including a wider, more integrative approach to the study of microvasculopathy under settings of elevated disease risk and following pharmacological intervention. A major benefit of integrating vascular health index is an increased understanding of the development, timing and efficacy of interventions through greater insight into integrated microvascular function in combination with individual, higher resolution metrics.

An Overview of Fog Data Analytics for IoT Applications

With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.

Implementation of an electronic solution to improve malnutrition identification and support clinical best practice

BACKGROUND

Routine malnutrition risk screening of patients is critical for optimal care and comprises part of the National Australian Hospital Standards. Identification of malnutrition also ensures reimbursement for hospitals to adequately treat these high-risk patients. However, timely, accurate screening, assessment and coding of malnutrition remains suboptimal. The present study aimed to investigate manual and digital interventions to overcome barriers to malnutrition identification for improvements in the hospital setting.

METHODS

Retrospective reporting on malnutrition identification processes was conducted through two stages: (1) manual auditing intervention and (2) development of a digital solution - the electronic malnutrition management solution (eMS). Repeated process audits were completed at approximately 6-monthly intervals through both stages between 2016 and 2019 and the results were analysed. In Stage 2, time investment and staff adoption of the digital solution were measured.

RESULTS

Overall, the combined effect of both regular auditing and use of the eMS resulted in statistically significant improvements across all six key measures: patients identified (97%-100%; p < 0.001), screened (68%-95%; p < 0.001), screened within 24 h (51%-89%; p < 0.001), assessed (72%-95%; p < 0.001), assessed within 24 h (66%-93%; p < 0.001) and coded (81%-100%; p = 0.017). The eMS demonstrated a reduction in screening time by over 60% with user adoption 100%. Data analytics enabled automated, real-time auditing with a 95% reduction in time taken to audit.

CONCLUSIONS

A single digital solution for management of malnutrition and automation of auditing demonstrated significant improvements where manual or combinations of manual and electronic systems continue to fall short.

Multiagent-Based Data Presentation Mechanism for Multifaceted Analysis in Network Management Tasks

Although network management tasks are highly automated using big data and artificial intelligence technologies, when an unforeseen cybersecurity problem or fault scenario occurs, administrators sometimes directly analyze system data to make a heuristic decision. However, a wide variety of information is required to address complex cybersecurity risks, whereas current systems are focused on narrowing the candidates of information. In this study, we propose a multiagent-based data presentation mechanism (MADPM) that consists of agents operating data-processing tools that store and analyze network data. Agents in MADPM interact with other agents to form data-processing sequences. In this process, we design not only the composition of the sequence according to requirements, but also a mechanism to expand it to enable multifaceted analysis that supports heuristic reasoning. We tested five case studies in the prototype system implemented in an experimental network. The results indicated that the multifaceted presentation of data can support administrators more than the selected single-faceted optimal presentation. The final outcome of our proposed approach is the provision of a multifaceted and cross-system data presentation for heuristic inference in network management tasks.