Interpretation Of Data Research Articles

Point of care lactate for differentiating septic shock from hypovolemic shock in non-ICU settings: a prospective observational study

BackgroundSeptic shock and hypovolemic shock are life-threatening illnesses that necessitate immediate recognition and intervention, as they can result in deadly consequences. While the underlying processes may vary, both entities can exhibit hypotension and organ dysfunction. No studies have been conducted on bedside testing to differentiate between these illnesses. Lactate measurement has been established as a viable option for early detection of septic shock. However, its role in diagnosing hypovolemic shock has yet to be evaluated. The aim of the study was to investigate alterations in lactate levels among diarrheal patients with septic shock and hypovolemic shock following the administration of first fluid resuscitation. MethodsWe conducted a prospective observational study in critically ill diarrheal adults aged ≥18 years in the emergency ward in Dhaka Hospital of icddr,b from 21st October 2021 to 31st May 2023 (total 19 months). The enrollment process was operational between 8:30 AM and 5:00 PM. Diarrheal adults with a diagnosis of sepsis with shock featured with poor peripheral perfusion (characterized by cold periphery and weak or absent pulse and capillary refill time >3 sec) or hypotension (characterized by mean arterial pressure <65 mm-Hg) were enrolled as cases and consecutive diarrheal patients without any obvious features of sepsis with hypovolemic shock (due to severe dehydration) comprised the comparison group.POC lactate test was done at hours 0, 1st and 6th by StatStrip Lactate meters (Nova Biomedical, US) to all enrolled patients. For comparison of POC lactate levels, we used paired t-test for comparing the lactate samples drawn at hour 0, hour 1 and 6 with the septic shock and hypovolemic shock group. Odds ratio (OR) and their 95% confidence intervals (CIs) were used to demonstrate the strength of association. The study was registered at Clinicaltrials.gov (NCT05108467) and received institutional ethical approval (PR-21097). FindingsOf 435 patients, 135 had septic shock and 141 had hypovolemic shock, rest 41 patient responded with fluid bolus. 25% (34/135) of the people in the septic shock group died whereas there is no mortality in the hypovolemic shock group. The number of patients visiting from outside Dhaka city had more septic shock than from inside were higher in comparison with (55% vs. 28%; p <0.001). Statistically significant difference was observed between septic shock and hypovolemic shock group for a median POC lactate in 0, 1st and 6th hours with an OR of 1.07 (95% CI: 0.99, 1.17; p=0.039); 1.48, (95% CI: 1.28, 1.70; p<0.001) and 2.36 (95% CI: 1.85, 3.00; p<0.001), respectively. The gradient of 1st to 2nd sample between septic shock and hypovolemic shock was found to be significantly different (OR:0.74, 95% CI: 0.64, 0.85; p<0.001). InterpretationPOC lactate test can detect septic shock by differentiating hypovolemic shock in diarrheal patients. By providing quick, reliable and accurate result this test can help clinicians quickly diagnose and treat time-sensitive condition, like septic shock. FundingThe study was funded by Global Affairs of Canada (GR-01726). The donors had no role in the design, implementation, analysis, data interpretation or writing manuscript, or decision to publish. The corresponding author had access to all data and takes responsibility for the final approval and submission of the manuscript.

Multi-axial fatigue of high-strength concrete: Model-enabled interpretation of punch-through shear test response

This paper aims to fill the gap in multi-axial fatigue characterization of concrete by presenting a comprehensive numerical analysis of data from the punch-through shear test (PTST), which allows control over combined shear and compression loads in the tested ligament. To accurately reproduce the degradation process in this multi-axial configuration, a material model must capture two key effects: (i) dissipative behavior at the inter-aggregate level during subcritical pulsating loading and (ii) fatigue-induced tri-axial stress redistribution in the test ligament. The recently published MS1 model by the authors effectively incorporates these features, thereby enabling a deeper interpretation of the experimental data. The former effect is seamlessly integrated into the model using the Lemaitre-type fatigue hypothesis, facilitating the direct derivation of general evolution equations. The latter effect is efficiently represented through the microplane homogenization framework. The model’s predictions for PTST response under monotonic shear loading with varying levels of radial confinement align well with experimental results. Similarly, the simulated fatigue response accurately reproduces the experimentally observed S-N (Wöhler) curves. Further studies demonstrate the model’s ability to predict the effect of fatigue loading sequence. A thermodynamically-based formulation provides an energetic interpretation of this effect, offering a quantitative breakdown of energy dissipation attributed to individual dissipative mechanisms over the lifetime of the material. The studies show that damage-induced dissipation remains almost constant regardless of different fatigue loading histories.

An Introduction to Types of Quasi-Experimental Designs.

Editor's note: This is the 24th article in a series on clinical research by nurses. The series is designed to be used as a resource for nurses to understand the concepts and principles essential to research. Each column will present the concepts that underpin evidence-based practice-from research design to data interpretation. To see all the articles in the series, go to https://links.lww.com/AJN/A204.

A data-driven operations management model: Implementing MIS for strategic decision making in tech businesses

In today’s rapidly evolving technological landscape, data-driven decision-making is critical for business growth and competitiveness. This paper presents a comprehensive operations management model that integrates Management Information Systems (MIS) to facilitate strategic decision-making in technology firms. The proposed model emphasizes the transformation of raw data into actionable insights, enabling organizations to optimize their operations and respond effectively to market dynamics. By systematically incorporating MIS into operational frameworks, the model provides a structured approach to data collection, analysis, and dissemination. It identifies key performance indicators (KPIs) that are essential for monitoring business performance and aligning operational activities with strategic objectives. Through case studies of leading tech companies, the model illustrates how data-driven methodologies can lead to improved forecasting, resource allocation, and overall operational efficiency. The framework is designed to be adaptable, allowing technology firms to customize the MIS components based on their unique business needs and market conditions. Key features of the model include real-time data analytics, predictive modeling, and user-friendly dashboards that enhance visibility across departments. These components empower decision-makers with timely and relevant information, fostering a culture of informed decision-making at all organizational levels. Moreover, the model addresses common challenges faced by tech businesses, such as data silos and integration issues. By promoting a centralized data repository and standardized reporting mechanisms, it enhances collaboration among teams and ensures consistency in data interpretation. The implications of this model extend beyond operational efficiency, influencing strategic initiatives such as product development, market expansion, and customer engagement. In conclusion, this data-driven operations management model not only supports strategic decision-making in technology firms but also positions them for sustainable growth in a competitive marketplace. By leveraging MIS effectively, organizations can harness the power of data to drive innovation and enhance overall business performance.

Development and validation of the post-CAR prognostic index for large B-cell lymphoma patients after CAR-T progression in third or later line treatment

Chimeric antigen receptor (CAR) T-cell therapy fails to achieve durable responses in over 60% of relapsed/refractory (R/R) large B-cell lymphoma (LBCL) patients in the third or later line setting. After CAR-T failure, survival outcomes are heterogeneous and a prognostic model in this patient population is lacking. A training cohort of 216 patients with progressive disease (PD) after CAR-T from 12 Spanish centers was used to develop the Post-CAR Prognostic Index (PC-PI); primary endpoint was overall survival (OS) from CAR-T progression. Validation was performed in an external cohort from three different European centers (n = 204). The prognostic score incorporated five variables, assessed at time of PD to CAR-T: ECOG (> 0), hemoglobin (< 10 g/dL), LDH (≥ 2xULN), number of extranodal sites (> 1) and time from CAR-T to PD (< 4 months). Patients were classified in four risk groups with distinct OS (p-value < 0.05 in all comparisons). In the validation cohort, median OS in the low (31%), intermediate-low (26%), intermediate-high (17%) and high risk (26%) were 15.7, 7.1, 1.8 and 1.0 months, respectively (p < 0.05 in all comparisons). Results were consistent following adjustment for subsequent treatment. In the external cohort, the PC-PI showed a C-statistic of 0.79 (95%CI 0.76–0.82), outperforming IPI and R-IPI. In conclusion, the PC-PI score is a novel tool for OS prediction and could facilitate risk-adapted management of LBCL patients relapsing after CAR T-cells. Additionally, these results will help stratification and interpretation of trials and real-world data incorporating CART-exposed patients.

1.Y.2. Children’s mental health problems explode. Can exposome research offer a solution?

Abstract Background A week-long, multidisciplinary workshop at the Lorentz Institute explored the expectations of exposome research on child health. Exposome research investigates how combined exposures affect health. Currently we see an increase in adolescent mental health problems in Europe. We explore at the workshop if and how exposome research can be part of the solution to tackle this health issue. Findings The workshop aims were to analyze how monitoring data from exposome research can be best used for improving health policy in relation to improving (environmental) health policy in relation to children’s health. By taking a deep dive into solution-finding, we explored creative solutions to better utilize existing exposome data from digital data platforms, such as cohort data. And to facilitate working across disciplines using innovative tools of working transdisciplinary across research, policymakers and other professionals. Furthermore, we study how to enhance, harmonize and interpretation of data on child health. Furthermore, we look how child health depends not only on the quality of a healthcare system, but also on the magnitude of negative or positive influencing factors, such as social and environmental factors. Identifying such stressors and reducing their impact through adequate policies can improve child health on local, national, or even global scales. One innovative way of identifying such stressors is the exposome approach. The exposome approach is the characterization of the non-genetic drivers of health and disease. The exposome comprises aspects of the Built environment (characteristics of where we live); the Social environment (with whom we interact, social networks and income); the Physio-Chemical environment (chemical and environmental factors exposure) and the Lifestyle/Food environment (what we eat, how much we exercise) over the entire life-course. Workshop aims: - Provide a background on the urgency of adolescent mental health problems in Europe. - Compare approaches of stakeholders and researchers: stakeholders’ input from the practical field can shape the approach of the research process. The second benefit is to derive implications for creating effective interventions and policies to prevent adverse effects of environmental exposures and to foster positive health in children and later in life. - Explore existing interventions on child health for issues, experiences and possible solutions to the governance and organization of health adaptation in urban and social planning. - How to act in the process of research to intervention to public policies. The workshop consists of three short presentations followed by interactive panel discussions. We end with the audience actively engaging in the debate, as well as entering their questions. The workshop will end by identifying the current and future necessary roles, responsibilities, capacities of the public health researchers, policymakers, and professionals for child health protection in complicated exposure settings. Key messages • Child mental health issues are increasing in an unexplainable way. • Engaging stakeholders into an exposome research process can help to form a child health strategy in Europe.

3.E. Scientific session: Environmental Health Tracking Systems: a tool for planetary health

Abstract Planetary health offers a good framework for better understanding the interconnection between human health and environmental changes, promoting effective cross-sector actions and partnerships, and ensuring policy coherence. Effective implementation of this conceptual framework requires monitoring and reporting on indicators relevant to planetary health that capture the scope, spatial and temporal scales of changes in natural systems that affect human health and well-being. An approach in that sense are the environmental public health tracking systems (EPHT), which imply the ongoing collection, integration, analysis, and interpretation of data at the same spatiotemporal scale about environmental hazards, and related human exposure and health effects, maintaining appropriate data protection measures. It aims to provide public-health decision makers with timely, accurate and systematic data for quantifying health impacts related to environmental factors. EPHT system are proposed as an essential tool for designing and monitoring policies and actions that prevent and reduce environmental health burdens (EHB) efficiently and cost-effectively, and as an alerting system against present and future climatic and health crisis. Pursuing such goals requires the use of health information systems that enable the monitoring and assessing of environmental health in multiple dimensions, considering specificities across different geographical and cultural settings. EPHT system requires defining a rationale on how to select the dimensions and indicators to be included. Common challenges for defining those indicators are related to the availability of data recording, data standardization, and the linkage, integration and sharing of databases. In parallel, it is also important to define a decision aiding tools in which evidence and data are understandable and inform about the extent to which specific policy goals are attained, tailoring such tools to the specific settings. Present session attends to answers to those challenges by sharing experiences from different contexts and countries Key messages • EPHT systems represent an effective tool for progressing in achieving planetary health goals and as a surveillance alerting system for future health crisis. • Adjusting EH data quality recording and analysis in EPHT systems is essential for improving resources assignment, and the effectiveness of healthy policies in reducing EHB.

Review of 3D electromagnetic modeling and inversion for mineral exploration

Many mineral deposits have low resistivity. This property makes the electromagnetic (EM) method a very useful tool for mineral exploration. In the past few decades, the application of EM exploration technologies has been reviewed by many researchers. However, most reviews focused on EM exploration methods, the development of equipment, or their applications. Three-dimensional forward modeling and inversions are high-accuracy EM interpretation techniques in which great progress has been made in recent years in mineral exploration. We review the development of EM technology for mineral exploration with a focus on 3D EM forward modeling, inversion technology, and its applications. We first briefly introduce the EM methods for mineral exploration. Next, we give a comprehensive review of 3D EM forward modeling, inversion, and data interpretation, with special attention paid to the development of EM theory and applications in mineral exploration. We hope this review can promote the application of 3D EM numerical simulation and inversion in mineral exploration.

10.K. Skills building seminar: Practical and methodological guidance for conducting online qualitative surveys in public health

Abstract Online qualitative surveys are surveys which prioritise qualitative (open text) questions in data collection. Researchers have stated that these types surveys have rich potential for qualitative researchers. They are able to collect rapid insights into new or emerging areas of health, and are an effective way of reaching populations who may not otherwise engage in qualitative interview studies or focus groups. Although these types of surveys are increasingly used by public health and health promotion researchers, there is very limited discussion in the research literature to guide researchers about the practical and methodological aspects of such surveys. This creates some challenges for qualitative researchers - as well as funders, and peer reviewers - in assessing the rigour and robustness of these studies. Using data from a range of online qualitative surveys that we have conducted with youth and adults relating to gambling, alcohol, climate change, and the commercial determinants of health, we describe the processes associated with developing, implementing, interpreting, and writing up online qualitative survey data. In this practical and interactive multimedia workshop and developing on the work of Braun, Clarke and colleagues, with a focus on Big Q interpretivist qualitative approaches. We will provide: 1. Practical examples, and lessons learned from our own studies about survey design, question development, the importance of rigorous piloting strategies, the use of novel techniques to prompt detailed responses from participants, and the decisions that are made about data cleaning. 2. Examples of the types of responses that can be expected in different types of surveys, and how decisions are made about the quality of responses to questions. 3. Guidance about how to analyse the volume of qualitative data that is generated from such surveys, including developing robust mechanisms to ensure trustworthiness of decision making and data interpretation. 4. Tips based on our role as journal editors about how to present qualitative survey data, and how to respond to challenging reviewer comments. Participants will be provided with a booklet containing survey templates, question examples, tips and examples of how to respond to reviewer comments. Key messages • Online qualitative surveys have rich potential for collecting insights about emerging public health issues. • This workshop will provide practical and methodological guidance for researchers who are interested in using these surveys. Speakers/Panelists Hannah Pitt Deakin University, Burwood, Australia Simone McCarthy Deakin University, Armadale, Australia Samantha Thomas Deakin University, Geelong, Australia

Addressing Semantic Variability in Clinical Outcome Reporting Using Large Language Models

Background/Objectives: Clinical trials frequently employ diverse terminologies and definitions to describe similar outcomes, leading to ambiguity and inconsistency in data interpretation. Addressing the variability in clinical outcome reports and integrating semantically similar outcomes is important in healthcare and clinical research. Variability in outcome reporting not only hinders the comparability of clinical trial results but also poses significant challenges in evidence synthesis, meta-analysis, and evidence-based decision-making. Methods: This study investigates variability reduction in outcome measures reporting using rule-based and large language-based models. It aims to mitigate the challenges associated with variability in outcome reporting by comparing these two models. The first approach, which is rule-based, will leverage well-known ontologies, and the second approach exploits sentence-bidirectional encoder representations from transformers (SBERT) to identify semantically similar outcomes along with Generative Pre-training Transformer (GPT) to refine the results. Results: The results show that the relatively low percentages of outcomes are linked to established rule-based ontologies. Analysis of outcomes by word count highlighted the absence of ontological linkage for three-word outcomes, which indicates potential gaps in semantic representation. Conclusions: Employing large language models (LLMs), this study demonstrates its ability to identify similar outcomes, even with more than three words, suggesting a crucial role in outcome harmonization efforts, potentially reducing redundancy and enhancing data interoperability.

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzingchanges in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learningand natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimatelyimproving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

Multivariate time-series clustering of cardiopulmonary exercise test data for cardiovascular risk stratification

Abstract Aims Current clinical practice assesses cardiorespiratory fitness (CRF) by evaluating summary indexes of cardiopulmonary exercise tests (CPET). Yet, the raw time series recordings may hold additional information with clinical relevance. In this study, we investigated if the interpretation of raw CPET data could identify distinct CRF phenogroups and improve risk stratification. Methods In 1399 participants (mean age, 56.4 years; 37.7% women), who underwent maximal CPET by cycle ergometer, we collected baseline clinical characteristics and information on incident cardiovascular (CV) events (n=297) on average 4.3 years later. We employed dynamic time warping combined with k-medoids to identify phenogroups from raw CPET time-series. To evaluate the clinical significance of the derived phenogroups we compared clinical characteristics and incidence of CV events, while an external population cohort (n=171) was used to further validate the trained model. Results The optimal number of clusters was 5. We observed significant differences in age, use of medication, spirometry indexes and disease prevalence across all clusters. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and history of CV disease while cluster 1 represented the most favourable risk profile. After adjusting for traditional clinical covariables, clusters 4 (HR: 1.30; 95%CI: 1.02-1.65; p=0.033) and 5 (HR: 1.36; 95%CI: 1.08-1.72; p=0.0088) had significantly higher risk for incident CV events compared to clusters 1 and 2. Comparing clinical characteristics across clusters in the external validation cohort revealed similar patterns. Conclusion – Employing unsupervised machine learning on time series CPET recordings, we identified clinically meaningful CRF phenogroups. An integrative CRF profiling might facilitate CV risk stratification and consequently risk management.

7.H. Round table: Advancing societal goals through health systems: Insights from the Renewed HSPA Framework

Abstract The purpose of this workshop, entitled ‘Advancing societal goals through health systems: Insights from the renewed HSPA framework,’ is to take a deeper look at the intricate relationships between health systems and broader societal objectives. We will dissect the contributions of health systems to societal well-being, a concept that stretches beyond mere economic growth to include social cohesion, environmental sustainability, and economic development. The session will examine the renewed global Health System Performance Assessment Framework, which serves as a tool for policymakers to better understand how health systems fit together and explains how they perform. This framework allows for a clearer interpretation of health data and provides a pathway to pinpoint health system issues and design that are most likely to enhance performance and resilience. The workshop will delve into the three dimensions of societal well-being-social cohesion, economic development, and environmental sustainability-nested within the HSPA framework. We will explore the health system’s role in advancing these dimensions, such as fostering community bonds, supporting economic growth through a healthy workforce, and contributing to the sustainable management of environmental resources. Integrating health system performance into societal goals is a practical obligation to create a resilient, cohesive, and economically robust society. This workshop will engage stakeholders in a discourse to translate this integration into multisectoral and actionable policies, aligning health system goals with societal aspirations for a sustainable future. This workshop will focus on: How health system assessments can capture contributions to societal goals, underpinning sustainable economic and environmental practices. Real-world case studies showcasing health systems’ role in fostering societal well-being and advancing public health policies. Interactive discussions will shed light on how health systems can be pivotal in achieving the Sustainable Development Goals, particularly in the face of modern challenges such as climate change, demographic shifts, and economic pressures. Key messages • Health systems play a pivotal role in advancing societal well-being beyond traditional health outcomes. • The renewed HSPA framework is a robust tool for policymakers to align health system performance with broader societal goals. Speakers/Panelists Scott Greer University of Michigan, European Observatory on Health Systems and Policies, Ann Arbor, USA Andrea Schmidt Austrian Public Health Institute, Vienna, Austria

Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence

Background: If left untreated, glaucoma—the second most common cause of blindness worldwide—causes irreversible visual loss due to a gradual neurodegeneration of the retinal ganglion cells. Conventional techniques for identifying glaucoma, like optical coherence tomography (OCT) and visual field exams, are frequently laborious and dependent on subjective interpretation. Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. Aims: The purpose of this study is to examine the current uses of AI in the early diagnosis, treatment, and detection of glaucoma while highlighting the advantages and drawbacks of different AI models and algorithms. In addition, it aims to determine how AI technologies might transform glaucoma treatment and suggest future lines of inquiry for this area of study. Methods: A thorough search of databases, including Web of Science, PubMed, and Scopus, was carried out to find pertinent papers released until August 2024. The inclusion criteria were limited to research published in English in peer-reviewed publications that used AI, ML, or DL to diagnose or treat glaucoma in human subjects. Articles were chosen and vetted according to their quality, contribution to the field, and relevancy. Results: Convolutional neural networks (CNNs) and other deep learning algorithms are among the AI models included in this paper that have been shown to have excellent sensitivity and specificity in identifying glaucomatous alterations in fundus photos, OCT scans, and visual field tests. By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. Nonetheless, several significant obstacles remain, such as the requirement for various training datasets, outside validation, decision-making transparency, and handling moral and legal issues. Conclusions: Artificial intelligence (AI) holds great promise for improving the diagnosis and treatment of glaucoma by facilitating prompt and precise interpretation of imaging data and assisting in clinical decision making. To guarantee wider accessibility and better patient results, future research should create strong generalizable AI models validated in various populations, address ethical and legal matters, and incorporate AI into clinical practice.

The information layer: shaping data into meaning with interactive tools

Abstract Valorization and interpretation of health data are contingent upon a robust analytical framework. This framework encompasses essential processes such as data cleaning, validation, and the critical evaluation of bias and missing data. A nuanced understanding of these factors is crucial for contextualizing data through stratification, developing comprehensive analysis plans, and addressing interactions, covariates, and confounders. Such meticulous methodologies guard against ‘data fishing expeditions’ that could otherwise lead to erroneous conclusions. Healthybelgium.be exemplifies our approach for deciding on appropriate levels of data aggregation and stratification. This study serves as a foundation for deriving conclusions and formulating hypotheses regarding the health status of the national and various sub-populations. Our discussion will cover the limitations of the questions that can be answered with the available data, the resulting opportunities for data enhancement, and the subsequent valorization of findings for knowledge advancement. Furthermore, we will present the interactive tools (through the use of R Shiny) developed by Sciensano to disseminate information broadly, making it accessible and comprehensible to diverse audiences. These tools embody a balance of strength and limitation, facilitating an informed dialogue among stakeholders. The ongoing refinement of these tools, influenced by continuous learning and insights, exemplifies our commitment to evolving public health intelligence.

The knowledge layer: the role of key messages for translating data into meaning

Abstract The large volume of data generated by health information systems often remains siloed within the institutions that produce it, posing significant challenges to its reuse and interpretation. This fragmentation can result in conflicting information on critical health topics, complicating the decision-making process for policymakers. To address this issue, Belgium has initiated the ‘Health Status Report’ website, hosted at healthybelgium.be. This website aims to synthesize and present health data in a manner that supports evidence-based policy formulation. The website adopts a structured approach to data integration and interpretation: initially, we identify and prioritize key health topics based on their relevance and the quality of available data. Subsequently, we merge and analyze data from diverse sources to discern patterns and trends that are crucial for public health monitoring. The insights gained are then presented through both narrative texts and interactive visualizations, facilitating access and understanding for a broad audience. Crucial to this process is ongoing communication with experts and stakeholders, ensuring that the data is not only accurately interpreted but also aligns with the needs and expectations of its users. We also aim to translate the raw data into meaningful key messages by consulting with internal and external experts. This interactive platform thus serves as a critical tool for translating complex health data into actionable knowledge, enabling informed public health strategies and interventions.

Response patterns in self-reported medical conditions: insights from the National Health and Nutrition Examination Survey (NHANES)

Abstract Background In the era of large-scale experiments, self-reported information has become a useful approach for gathering information longitudinally. The extent of subjectivity in self-reported medical conditions can vary considerably and can subject to a large degree of individual interpretation. Purpose To assess potential clustering among non-related self-reported conditions in the National Health and Nutrition Examination Survey (NHANES) over a period of 20 years (1999-2018). Methods We considered adult individuals (older than 20 years old) at the time of recruitment in all 10 cycles of the NHANES spanning from 1999 to 2018. For each individual, we gathered information on demographics, socioeconomic, examination data, laboratory variables and self-reported conditions included in the questionnaire instruments being available for all cycles. We focused only on questionnaire variables that are valid questions for both men and women. We examined potential patterns in clustering of responses among individuals in the same survey cycle and subsequently among individuals of different survey cycles. Results The study sample consisted of 55081 adult individuals. Female and male participants were equally represented in the study sample (52% and 48% respectively). The mean age of individuals at screening was 50 years (standard deviation 18). Participants were mostly non-Hispanic white (44%), followed by non-Hispanic black (21%) and Mexican American (17%). 41 self-reported medical conditions were available across the 10 cycles of NHANES and were considered in this analysis. The percentage of affirmative answers and missing information in the level of self-reported medical condition did not differ considerably across the 10 cycles for the vast majority of the medical conditions; but the percentage of missing information differed considerably across the different self-reported conditions and cycles ranging between 0% to 97% (Figure). Conclusions Surveys based on self-reported medical conditions may suffer from clusters and secular trends in provided responses or missing information among non-related conditions. Potential clustering may bias the data analysis and interpretation of the findings. The risk of clustering in individuals responses should be taken into account in analysis of self-reported conditions.Figure

Evaluation of Inherent Damping Feature of Beam Structure Based on Complex Modulus

Structural damping, which has been widely used to quantify the energy dissipation of engineering structures, plays an important role in dynamic analysis during the design phase and the assessment of existing structures. However, the development of an accurate damping model remains an open question due to the unclear mechanism. In this study, structural damping was identified based on loss factors for beam structures, which were used for presenting the damping behavior during vibrations. The equation of motion for the bending deformation of a Timoshenko beam was derived, accounting for the material damping. To identify the loss factor of the beam from the response signals at a limited number of measurement points, an inverse algorithm based on wave coefficient estimation was developed, considering the numerical stability for data interpretation. Based on the proposed loss factor identification method, several beam models with different shapes of cross-sections were investigated, focusing on the feasibility of the method of varying cross-section cases. Comparing the spectral element model with the loss factor and the finite element model with Rayleigh damping, it was confirmed that the loss factor can better describe the actual energy dissipation behavior of the structure. It provides a more accurate theoretical model for estimating structural damping in practical engineering.

Large language models to facilitate pregnancy prediction after invitro fertilization.

We evaluated the efficacy of large language models (LLMs), specifically, generative pre-trained transformer-4 (GPT-4), in predicting pregnancy following invitro fertilization (IVF) treatment and compared its accuracy with results from an original published study. Our findings revealed that GPT-4 can autonomously develop and refine advanced machine learning models for pregnancy prediction with minimal human intervention. The prediction accuracy was 0.79, and the area under the receiver operating characteristic curve (AUROC) was 0.89, exceeding or being at least equivalent to the metrics reported in the original study, that is, 0.78 for accuracy and 0.87 for AUROC. The results suggest that LLMs can facilitate data processing, optimize machine learning models in predicting IVF success rates, and provide data interpretation methods. This capacity can help bridge the knowledge gap between data scientists and medical personnel to solve the most pressing clinical challenges. However, more experiments on diverse and larger datasets are needed to validate and promote broader applications of LLMs in assisted reproduction.

Implementation of Business Intelligence for Analysis Data of Drug Sales Using Exploratory Data Analysis (EDA) and Visualization Data Using Looker Studio

This study aims to analyze pharmaceutical sales performance by leveraging big data and advanced information technology tools, providing insights for improved business strategies. Using the Exploratory Data Analysis (EDA) method, the study processes raw sales data through spreadsheet applications to identify key patterns and trends. The findings are then visualized with Looker Studio on an intelligent dashboard tailored to the needs of the marketing team. The dashboard enables quick, data-driven decision-making by displaying performance metrics and trends relevant to sales and marketing strategies. The results reveal critical insights into sales behavior, including high-performing products, regional sales variations, and temporal sales trends. The analysis equips the marketing team with actionable data to refine their strategies, prioritize product focus, and adjust marketing efforts in response to identified patterns. In conclusion, the use of EDA and data visualization provides a structured approach to big data interpretation, allowing the company to optimize business and marketing performance. The study underscores the potential of data-driven dashboards to enhance strategic planning, suggesting broader applications in various data-intensive business environments.