Influence of Air Pollutants on the Disease Activity and Quality of Life in Rheumatoid Arthritis, an Iranian Observational Longitudinal Study.

Background: Environmental exposures and genetic predisposition interactions may result in autoimmune rheumatic diseases. This study aimed to determine the effect of outdoor air pollutants on the activity of rheumatoid arthritis (RA) in a longitudinal follow-up. Methods: We longitudinally studied 50 patients with RA bimonthly over 6 months in Mashhad, one of the most polluted cities in Iran. Disease activity and health-related quality of life (HRQoL) were examined according to the disease activity score (DAS28ESR), health assessment questionnaires (HAQ), physical health component summary (PCS), and visual analogue scale (VAS) criteria. The outdoor air pollutant was measured by monitoring the average concentration of nitrogen oxide (NO), carbon monoxide (CO), O2 level, Sulfur dioxide (SO2), and some particles less than 10 and 2.5 micrometers in diameter (PM <10 µm, PM <2.5 µm). The temperature and humidity levels were also measured. The univariate and multivariate statistical analyses were used for data analysis and the role of confounding factors was determined using the generalized estimation equation method. Results: Statistical analysis indicated a significant increase of the DAS28ESR (B = 0.04 [0.08]; P = 0.01) and VAS (B = 4.48 [1.73]; P = 0.01) by CO concentration. Moreover, a number of polluted days increased the VAS in patients. In addition, other air pollutants, temperature, and humidity were not affected significantly by the DAS28ESR and quality of life indexes by considering confounders such as medications, age, and job. Conclusion: Based on our findings, CO concentration was the only effective outdoor air pollutant that could increase RA disease activity. In addition, CO concentration and the number of polluted days make patients feel more ill. As the role of indoor air pollutants is highly important, further research on this critical topic is required to establish the role of air pollution on RA disease activity.

Building a Disease Knowledge Graph.

Knowledge graphs have proven themselves as a robust tool in clinical applications to aid patient care and help identify treatments for new diseases. They have impacted many information retrieval systems in healthcare. In this study, we construct a disease knowledge graph using Neo4j (a knowledge graph tool) for a disease database to answer complex questions that are time-consuming and labour-intensive to be answered in the previous system. We demonstrate that new information can be inferred in a knowledge graph based on existing semantic relationships between the medical concepts and the ability to perform reasoning in the knowledge graph.

Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets.

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

Effects of Air Pollution on Disease Activity and Health-Related Quality of Life of Systemic Lupus Erythematous Patients: An Iranian Observational Longitudinal Study.

INTRODUCTION: Air pollution is one of the environmental factors that influences the pathogenesis of systemic autoimmune diseases, followed by the development and spread of inflammation and increased oxidative damage. Only a few studies have been conducted on the impact of air pollution on disease activity in patients with lupus, which mostly have focused on PM2.5 particles. MATERIALS AND METHODS: We longitudinally studied 50 patients with lupus bimonthly in a 6-month period in Mashhad, one of the polluted cities of Iran. Disease activity and quality of life were examined considering SLEDAI2K, SLEQOL, and VAS criteria. The outdoor air pollutant was measured by monitoring the average concentration of nitrogen dioxide (NO2), carbon monoxide (CO), some particles less than 10 and 2.5 micrometers in diameter (PM <10, PM <2.5) and the level of temperature and humidity which were taken from the Meteorological Organization of Mashhad. Confounding factors such as medications were investigated by univariate and multivariate statistical analysis, specifically by GEE method. RESULTS: The possible relation among various factors to SLEDAI, SLEQOL and VAS by two different univariate and multivariate analyses were studied. Our analysis indicated that spring season, decreased temperature, increased air pollutants including (PM2.5, and NO2) and increased humidity increase SLEDAI2K. Furthermore, the percent of polluted days directly correlates with Anti-dsDNA and NO2 significantly increases SLEQOL. CONCLUSION: Based on our findings, air pollution (particularly NO2 and PM2.5) has affected at least some aspects of the disease and the health-related quality of life (HRQL) of lupus patients. Further research is needed to confirm these findings.

Analyzing systemic lupus erythematosus publications using neural network-based multi-label classification algorithms.

The heterogeneity in systemic lupus erythematosus research topics poses a challenge for the entire lupus community, from basic geneticists to clinical investigators. As such, it is critical for medical professionals to remain up to date on directions in lupus research and the main fields in which this research is being conducted (e.g., etiology, diagnosis, treatment, and outcomes). This article develops two multi-label text-classification models using Deep Neural Networks and Convolutional Neural Networks to classify the human-based adult-onset lupus-related articles in the PubMed database based on their abstract, keywords, and MeSH terms. During training evaluation, our models correctly indicated all relevant labels for 70% of the articles. The applied machine learning models (Deep Neural Network and Convolutional Neural Network) yielded a Micro-F1 score of 0.89, meaning that it successfully labeled the most relevant medical domains and types. In addition, these types of studies help the researchers be aware of the essential topics in this field, but due to difficulties in designing, the related studies are ignored or fade.

Towards a Knowledge Graph-Based Explainable Decision Support System in Healthcare.

The decisions derived from AI-based clinical decision support systems should be explainable and transparent so that the healthcare professionals can understand the rationale behind the predictions. To improve the explanations, knowledge graphs are a well-suited choice to be integrated into eXplainable AI. In this paper, we introduce a knowledge graph-based explainable framework for AI-based clinical decision support systems to increase their level of explainability.

Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics.

The aim of this paper is to investigate semantic web based methods to enrich and transform a medical discussion forum in order to perform semantics-driven social network analysis. We use the centrality measures as well as semantic similarity metrics to identify the most influential practitioners within a discussion forum. The centrality results of our approach are in line with centrality measures produced by traditional SNA methods, thus validating the applicability of semantic web based methods for SNA, particularly for analyzing social networks for specialized discussion forums.