Interactive Web-based Platform Research Articles

Mining of Data in Real Time to build an Interactive Research Analytics Dashboard

The paper discusses the process of building a web-based interactive platform to assess the research productivity of the individual faculty of an institution. The application harvests the bibliographic data of researchers’ publications from the Scopus database and visualises it in real-time. The open-source environment R with different packages like the shiny, shiny dashboard for building web apps and dashboard designing; httr for data harvesting through API, jsonlite for parsing JSON data, tm for text mining, wordcloud for visualising wordcloud, textstem for lemmatization of text strings, dplyr for data manipulation, etc. were used. The data was harvested at the backend using Scopus API. The user interface of the application’s front end is the default design of the shiny dashboard, with some limited customization using CSS. The system tracks and visualizes the research productivity of the faculty. The application provides the total number of publications, citations, H Index, open access information, number of publications in open access, and closed access, in an assessment year rather than a calendar year, source-wise differentiation of publications, annual distribution of publications, common themes of the research, etc.

Using a smart city framework to advance justice: applying an equity lens to compare air quality in neighbourhoods of different socioeconomic profiles

ABSTRACT The smart city framework, while initially received with enthusiasm, has been critiqued for reproducing urban inequalities rather than reducing marginalisation. This paper demonstrates how application of an equity lens within a smart city framework can advance justice by addressing air quality inequities in a diverse coastal city. We conducted a pilot study in which integrated air quality sensors are deployed for criteria pollutants in two contrasting neighbourhoods, to demonstrate that the smart city framework can be used with an equity lens for baseline data collection and analysis to support communities of colour in advocating on their own behalf. Communities of colour are disproportionately impacted by poor air quality, pollution exposure, and health burdens. Research shows that marginalised communities often lack air quality sensors installed by traditional regulatory measures. Thus, smart city technology using targeted deployment of air quality sensors can aid neighbourhood-level sampling, and data can be leveraged by marginalised communities for policy development to address inequities in pollution exposure and health impacts. In this study, air quality sensors were deployed and integrated with a web-based interactive platform called Community Resiliency Information Systems (CRIS) to compare a white, affluent neighbourhood with a marginalised, predominantly Black neighbourhood in Saint Petersburg, Florida. Data collected indicates significant differences in concentrations of air pollutants in the study neighbourhoods; exposure levels are higher in the poor, minority-dominated neighbourhood. CRIS facilitates two-way communication between residents and policy makers, demonstrating that careful application of technology can produce data to support community advocacy and address environmental inequalities. Key policy highlights The smart city framework integrated with air quality sensors has the potential to advance justice when applied through an equity lens. This pilot project demonstrates air quality inequities at neighbourhood level comparisons (affluent vs marginalised) in a coastal city – exposure levels are higher in the poor, minority-dominated neighbourhood. The CRIS platform facilitates two-way communication between residents and policy makers, demonstrating that careful application of technology can produce data to support community advocacy and address environmental inequalities.

ResearchConnect.info: An Interactive Web-Based Platform for Building Academic Collaborations

Radiology is a rapidly evolving field that benefits from continuous innovation and research participation among trainees. Traditional methods for involving residents in research are often inefficient and limited, usually due to the absence of a standardized approach to identifying available research projects. A centralized online platform can enhance networking and offer equal opportunities for all residents. Research Connect is an online platform built with PHP, SQL, and JavaScript. Features include project and collaboration listing as well as advertisement of project openings to medical/undergraduate students, residents, and fellows. The automated system maintains project data and sends notifications for new research opportunities when they meet user preference criteria. Both pre- and post-launch surveys were used to assess the platform's efficacy. Before the introduction of Research Connect, 69% of respondents used informal conversations as their primary method of discovering research opportunities. One year after its launch, Research Connect had 141 active users, comprising 63 residents and 41 faculty members, along with 85 projects encompassing various radiology subspecialties. The platform received a median satisfaction rating of 4 on a 1-5 scale, with 54% of users successfully locating projects of interest through the platform. Research Connect addresses the need for a standardized method and centralized platform with active research projects and is designed for scalability. Feedback suggests it has increased the visibility and accessibility of radiology research, promoting greater trainee involvement and academic collaboration.

Design of an interactive fashion recommendation platform with intelligent systems

Design of an interactive fashion recommendation platform with intelligent systems

Designing web-based designed for decision making process of upgrading slum sanitation

In Indonesia’s slum areas, Inadequate sanitation is a major challenge. This study introduces an interactive web-based platform, UPSS (“Upgrading Slum Sanitation”), to aid in selecting appropriate sanitation technologies for these regions. UPSS integrates a Decision Support System (DSS) to streamline the decision-making process. Our objective was to create UPSS to facilitate the selection of suitable sanitation technologies, tailored to the specific needs of Indonesia slum areas. We employed a mixed-methods approach to develop UPSS, focusing on user-friendly interfaces and streamlined decision-making processes. The first screen will help identify the slum classification (high, middle and low slum) by using the questionnaire. The following screens will identify alternative sanitation technologies. After that we apply the UPSS in the case study area, Manado Indonesia. The application of UPSS in slum areas of Indonesia demonstrated its effectiveness in reducing planning time for sanitation improvement. The results, presented in an easily interpretable format, showed a range of sanitation options and were well-received by facilitators in these areas. UPSS proves significant in improving sanitation planning in slums. It offers a quick, efficient, and user-friendly tool for stakeholders to identify and implement the most suitable sanitation technologies, thereby addressing a critical need in urban sanitation management

IProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry–based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature–related, and correlation analyses, including protein–protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein–protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

Improving Patient-Reported Outcomes in Stroke Care using Remote Blood Pressure Monitoring and Telehealth

Abstract Background Inequities in health care access leads to suboptimal medication adherence and blood pressure (BP) control. Informatics-based approaches may deliver equitable care and enhance self-management. Patient-reported outcomes (PROs) complement clinical measures to assess the impact of illness on patients' well-being in poststroke care. Objectives The aim of this study was to determine the feasibility of incorporating PROs into Telehealth After Stroke Care (TASC) and to explore the effect of this team-based remote BP monitoring program on psychological distress and quality of life in an underserved urban setting. Methods Patients discharged home from a Comprehensive Stroke Center were randomized to TASC or usual care for 3 months. They were provided with a BP monitor and a tablet that wirelessly transmitted data to a cloud-based platform, which were integrated with the electronic health record. Participants who did not complete the tablet surveys were contacted via telephone or e-mail. We collected the Patient-Reported Outcomes Measurement Information System Managing Medications and Treatment (PROMIS-MMT), Patient Activation Measure (PAM), Neuro-QOL (Quality of Life in Neurological Disorders) Cognitive Function, Neuro-QOL Depression, and Patient Health Questionnaire-9 (PHQ-9). T-tests and linear regression were used to evaluate the differences in PRO change between the arms. Results Of the 50 participants, two-thirds were Hispanic or non-Hispanic Black individuals. Mechanisms of PRO submission for the arms included tablet (62 vs. 47%), phone (24 vs. 37%), tablet with phone coaching (10 vs. 16%), and e-mail (4 vs. 0%). PHQ-9 depressive scores were nominally lower in TASC at 3 months compared with usual care (2.7 ± 3.6 vs. 4.0 ± 4.1; p = 0.06). No significant differences were observed in PROMIS-MMT, PAM, or Neuro-QoL measures. Conclusion Findings suggest the feasibility of collecting PROs through an interactive web-based platform. The team-based remote BP monitoring demonstrated a favorable impact on patients' well-being. Patients equipped with appropriate resources can engage in poststroke self-care to mitigate inequities in health outcomes.

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations.

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease. The code and datasets are publicly available at https://github.com/jinyangwu/KGETCDA.

Impact of a digital web-based asthma platform, a real-life study

BackgroundDigital health technology (DHT) is a growing area in the treatment of chronic diseases. Study results on DHT’s effect on asthma control have been mixed, but benefits have been seen for adherence, self-management, symptoms, and quality of life. The aim was to evaluate the impact of an interactive web-based asthma treatment platform on asthma exacerbations and health care visits.MethodsIn this real-life study, we retrospectively collected data on adult patients registered on a web-based interactive asthma treatment platform between December 2018 and May 2021. Patients who activated their accounts were active users, and patients who did not were inactive users and considered as controls. We compared the number of exacerbations, total number of exacerbation events defined as the sum of oral corticosteroid (OCS) and antimicrobial courses, emergency room visits, hospitalizations, and asthma-related health care visits before and one year after the registration on the platform. Statistical tests used included the t-test, Pearson’s chi-square test and Poisson regression models.ResultsOf 147 patients registered on the platform, 106 activated their accounts and 41 did not. The active users had significantly fewer total number of exacerbation events (2.56 per person years, relative decline 0.78, 95% CI 0.6 to 1.0) and asthma-related health care visits (2.38 per person years, relative decline 0.84, 95% CI 0.74 to 0.96) than before registration to the platform, whereas the reductions in health care visits and the total number of exacerbation events were not significant in the inactive users.ConclusionsAn interactive web-based asthma platform can reduce asthma-related health care visits and exacerbations when used actively.

Web-Based Rapid Application Development (RAD) for Marketing of Ende Lio Traditional Bond Motif Woven Fabric

The marketing of Ende Lio traditional bond motif ikat woven fabric predominantly relies on conventional market stalls. However, with the growing demand for diverse products derived from ikat weaving, such as garments, bags, tunics, and accessories, there is a need for a more efficient marketing strategy. This study aims to implement Rapid Application Development (RAD) and design a web-based marketing system for the Marketing of Ende Lio Traditional Bond Motif Woven Fabric to enhance accessibility and increase public awareness of the products. The objectives of this research include developing an intuitive and interactive web-based platform for marketing Ende Lio's woven ikat fabrics, improving the efficiency of the marketing process, ensuring a seamless user experience, and evaluating the system's impact on sales and customer satisfaction. The RAD methodology, renowned for its incremental and time-constrained software development approach, was employed. The design process involved the use of Use Case Diagrams, and unit testing was conducted to validate the functionality and performance of the application.

Experiences of Using an Electronic Health Tool Among Health Care Professionals Involved in Chronic Obstructive Pulmonary Disease Management: Qualitative Analysis.

Chronic obstructive pulmonary disease (COPD) is one of the most common and deadliest chronic diseases of the 21st century. eHealth tools are seen as a promising way of supporting health care professionals in providing evidence-based COPD care, for example, by reinforcing information and interventions provided to the patients and providing easier access and support to the health care professional themselves. Still, knowledge is scarce on the experience of using eHealth tools from the perspective of the health care professional involved in COPD management. The study explored the experiences of using an eHealth tool among health care professionals that worked with patients with COPD in their daily clinical practice. This exploratory qualitative study is part of a process evaluation in a parallel group, controlled, pragmatic pilot trial. Semistructured interviews were performed with 10 health care professionals 3 and 12 months after getting access to an eHealth tool, the COPD Web. The COPD Web, developed using cocreation, is an interactive web-based platform that aims to help health care professionals provide health-promoting strategies. Data from the interviews were analyzed using qualitative content analysis with an inductive approach. The main results reflected health care professionals' experiences in 3 categories: receiving competence support and adjusting practice, improving quality of care, and efforts required for implementation. These categories highlighted that using an eHealth tool such as the COPD Web was experienced to provide knowledge support for health care professionals that led to adaptation and facilitation of working procedures and person-centered care. Taken together, these changes were perceived to improve the quality of care through enhanced patient contact and encouragement of interprofessional collaboration. In addition, health care professionals expressed that patients using the COPD Web were better equipped to tackle their disease and adhered better to provided treatment, increasing their self-management ability. However, structural and external barriers bar the successful implementation of an eHealth tool in daily praxis. This study is among the first to explore experiences of using an eHealth tool among health care professionals involved in COPD management. Our novel findings highlight that using an eHealth tool such as the COPD Web may improve the quality of care for patients with COPD (eg, by providing knowledge support for health care professionals and adapting and facilitating working procedures). Our results also indicate that an eHealth tool fosters collaborative interactions between patients and health care professionals, which explains why eHealth is a valuable means of encouraging well-informed and autonomous patients. However, structural and external barriers requiring time, support, and education must be addressed to ensure that an eHealth tool can be successfully implemented in daily praxis. ClinicalTrials.gov NCT02696187; https://clinicaltrials.gov/ct2/show/NCT02696187.

Yersiniomics, a Multi-Omics Interactive Database for Yersinia Species.

The genus Yersinia includes a large variety of nonpathogenic and life-threatening pathogenic bacteria, which cause a broad spectrum of diseases in humans and animals, such as plague, enteritis, Far East scarlet-like fever (FESLF), and enteric redmouth disease. Like most clinically relevant microorganisms, Yersinia spp. are currently subjected to intense multi-omics investigations whose numbers have increased extensively in recent years, generating massive amounts of data useful for diagnostic and therapeutic developments. The lack of a simple and centralized way to exploit these data led us to design Yersiniomics, a web-based platform allowing straightforward analysis of Yersinia omics data. Yersiniomics contains a curated multi-omics database at its core, gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets for Yersinia species. It integrates genomic, transcriptomic, and proteomic browsers, a genome viewer, and a heatmap viewer to navigate within genomes and experimental conditions. For streamlined access to structural and functional properties, it directly links each gene to GenBank, the Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, InterPro, IntAct, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and each experiment to Gene Expression Omnibus (GEO), the European Nucleotide Archive (ENA), or the Proteomics Identifications Database (PRIDE). Yersiniomics provides a powerful tool for microbiologists to assist with investigations ranging from specific gene studies to systems biology studies. IMPORTANCE The expanding genus Yersinia is composed of multiple nonpathogenic species and a few pathogenic species, including the deadly etiologic agent of plague, Yersinia pestis. In 2 decades, the number of genomic, transcriptomic, and proteomic studies on Yersinia grew massively, delivering a wealth of data. We developed Yersiniomics, an interactive web-based platform, to centralize and analyze omics data sets on Yersinia species. The platform allows user-friendly navigation between genomic data, expression data, and experimental conditions. Yersiniomics will be a valuable tool to microbiologists.

European fitness landscape for children and adolescents: updated reference values, fitness maps and country rankings based on nearly 8 million test results from 34 countries gathered by the FitBack network

Objectives(1) To develop reference values for health-related fitness in European children and adolescents aged 6–18 years that are the foundation for the web-based, open-access and multilanguage fitness platform (FitBack); (2)...

Multicentre, randomised, economic evaluation of a web-based interactive education platform, simple or enhanced, for patients with end-stage renal disease: the PIC-R trial protocol

IntroductionEnd-stage renal disease (ESRD) affects 84 000 persons in France and costs an estimated €4.2 billion. Education about their disease empowers patients and allows improved management of their disease and...

IPOscore: An interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain

Background: The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. Objectives: The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. Results: The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. Conclusions: The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org.

A real-time web tool for monitoring and mitigating indoor airborne COVID-19 transmission risks at city scale.

Airborne transmission of aerosols contributes to a large portion of the SARS-CoV-2 spread indoors. This study develops a real-time interactive web-based platform for the public to compare various strategies to curb indoor airborne transmission of COVID-19 in different archetype buildings at a city scale. Although many countries have started vaccination and a gradual re-opening, because of emerging new variants of the virus and the possibility of future pandemics, a lively updated tool for monitoring and mitigation of infection risk is essential. As a demonstration, we evaluated the impacts of six mitigation measures on the infection risks in various building types in a city. It shows that the same strategy could perform quite differently, depending on building types and properties. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the minimum required outdoor air ventilation rate is not as effective as other strategies to reduce the risk. It also causes considerable penalties on energy consumption. Therefore, new building ventilation standards, control actions, and design criteria should be considered to mitigate the infection risk and save energy.

A framework for integrated energy and exposure to ambient pollution (iEnEx) assessment toward low-carbon, healthy, and equitable cities

• A framework to explore urban energy and exposure disparities in cities introduced. • Hourly spatiotemporal LUR models with gradient boosting machine developed. • Local environmental data captured from Chicago's smart cities platforms. • Correlation between spatial disparities of energy burden and exposure to PM2.5 captured. • Households with higher energy burden have less opportunity to use natural ventilation. This research develops a framework for integrated energy and exposure to ambient pollution (iEnEx) assessment addressing energy, health, and equity as an interconnected problem within the built environment. Its focus is to explore spatial disparities of households’ energy burden and their exposure to ambient particulate matters (PM 2.5 as end point) in supporting energy efficiency goals of cities. In this study, energy burden is defined as energy affordability of households for paying energy costs. And natural ventilation (NV) is examined as a catalyst of building energy efficiency to measure tradeoffs between energy burden mitigation and exposure to ambient PM 2.5 pollution. The framework is built upon a five-step workflow using spatiotemporal land use regression (LUR) with gradient boosting machine (GBM) and bringing human behavioral patterns along with array of urban big data from smart cities platforms into the model to improve the explanatory capacities of the exposure model. We tested Chicago, IL as a case study. Findings indicate the effectiveness of the proposed framework in integrating urban energy and human health systems for envisioning urban building energy reduction goals. A web-based interactive platform is designed to communicate the results. iEnEx framework can assist designers, engineers, planners, and policymakers in better understanding these systems from environmental and social lenses in the context of equity within the built environment to ensure future sustainable cities.

CATHAI: cluster analysis tool for healthcare-associated infections.

Whole genome sequencing (WGS) is revolutionizing disease surveillance where it facilitates high-resolution clustering of related organism and outbreak detection. However, visualizing and efficiently communicating genomic data back to clinical staff is crucial for the successful deployment of a targeted infection control response. CATHAI (cluster analysis tool for healthcare-associated infections) is an interactive web-based visualization platform that couples WGS informed clustering with associated metadata, thereby converting sequencing data into informative and accessible clinical information for the management of healthcare-associated infections (HAI) and nosocomial outbreaks. All code associated with this application are free available from https://github.com/FordeGenomics/cathai. A demonstration version of CATHAI is available online at https://cathai.fordelab.com.

The transparent minds: methods of creation of 3D digital models from patient specific data

This paper focuses on the method for creating 3-dimensional (3D) digital models extracted from patient- specific scans of the brain. The described approach consists of several cross-platform stages: raw data segmentation, data correction in 3D-modelling software, post-processing of the 3D digital models and their presentation on an interactive web-based platform. This method of data presentation offers a cost and time effective option to present medical data accurately. An important aspect of the process is using real patient data and enriching the traditional slice-based representation of the scans with 3D models that can provide better understanding of the organs’ structures. The resulting 3D digital models also form the basis for further processing into different modalities, for example models in Virtual Reality or 3D physical model printouts. The option to make medical data less abstract and more understandable can extend their use beyond diagnosis and into a potential aid in anatomy and patient education. The methods presented in this paper were originally based on the master thesis ‘Transparent Minds: Testing for Efficiency of Transparency in 3D Physical and 3D Digital Models’, which focussed on creating and comparing the efficiency of transparent 3D physical and 3D digital models from real-patient data.

Development of a data classification system for preterm birth cohort studies: the RECAP Preterm project

BackgroundThe small sample sizes available within many very preterm (VPT) longitudinal birth cohort studies mean that it is often necessary to combine and harmonise data from individual studies to increase statistical power, especially for studying rare outcomes. Curating and mapping data is a vital first step in the process of data harmonisation. To facilitate data mapping and harmonisation across VPT birth cohort studies, we developed a custom classification system as part of the Research on European Children and Adults born Preterm (RECAP Preterm) project in order to increase the scope and generalisability of research and the evaluation of outcomes across the lifespan for individuals born VPT.MethodsThe multidisciplinary consortium of expert clinicians and researchers who made up the RECAP Preterm project participated in a four-phase consultation process via email questionnaire to develop a topic-specific classification system. Descriptive analyses were calculated after each questionnaire round to provide pre- and post- ratings to assess levels of agreement with the classification system as it developed. Amendments and refinements were made to the classification system after each round.ResultsExpert input from 23 clinicians and researchers from the RECAP Preterm project aided development of the classification system’s topic content, refining it from 10 modules, 48 themes and 197 domains to 14 modules, 93 themes and 345 domains. Supplementary classifications for target, source, mode and instrument were also developed to capture additional variable-level information. Over 22,000 individual data variables relating to VPT birth outcomes have been mapped to the classification system to date to facilitate data harmonisation. This will continue to increase as retrospective data items are mapped and harmonised variables are created.ConclusionsThis bespoke preterm birth classification system is a fundamental component of the RECAP Preterm project’s web-based interactive platform. It is freely available for use worldwide by those interested in research into the long term impact of VPT birth. It can also be used to inform the development of future cohort studies.