179 research outputs found
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
In this paper have developed a novel hybrid hierarchical attention-based
bidirectional recurrent neural network with dilated CNN (HARDC) method for
arrhythmia classification. This solves problems that arise when traditional
dilated convolutional neural network (CNN) models disregard the correlation
between contexts and gradient dispersion. The proposed HARDC fully exploits the
dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM)
architecture to generate fusion features. As a result of incorporating both
local and global feature information and an attention mechanism, the model's
performance for prediction is improved.By combining the fusion features with a
dilated CNN and a hierarchical attention mechanism, the trained HARDC model
showed significantly improved classification results and interpretability of
feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score
normalization, filtering, denoising, and segmentation are used to prepare the
raw data for analysis. CGAN (Conditional Generative Adversarial Network) is
then used to generate synthetic signals from the processed data. The
experimental results demonstrate that the proposed HARDC model significantly
outperforms other existing models, achieving an accuracy of 99.60\%, F1 score
of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH
generated ECG. In addition, this approach substantially reduces run time when
using dilated CNN compared to normal convolution. Overall, this hybrid model
demonstrates an innovative and cost-effective strategy for ECG signal
compression and high-performance ECG recognition. Our results indicate that an
automated and highly computed method to classify multiple types of arrhythmia
signals holds considerable promise.Comment: 23 page
Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19
Background: Providing appropriate care for people suffering from COVID-19,
the disease caused by the pandemic SARS-CoV-2 virus is a significant global
challenge. Many individuals who become infected have pre-existing conditions
that may interact with COVID-19 to increase symptom severity and mortality
risk. COVID-19 patient comorbidities are likely to be informative about
individual risk of severe illness and mortality. Accurately determining how
comorbidities are associated with severe symptoms and mortality would thus
greatly assist in COVID-19 care planning and provision.
Methods: To assess the interaction of patient comorbidities with COVID-19
severity and mortality we performed a meta-analysis of the published global
literature, and machine learning predictive analysis using an aggregated
COVID-19 global dataset.
Results: Our meta-analysis identified chronic obstructive pulmonary disease
(COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2
diabetes, malignancy, and hypertension as most significantly associated with
COVID-19 severity in the current published literature. Machine learning
classification using novel aggregated cohort data similarly found COPD, CVD,
CKD, type 2 diabetes, malignancy and hypertension, as well as asthma, as the
most significant features for classifying those deceased versus those who
survived COVID-19. While age and gender were the most significant predictor of
mortality, in terms of symptom-comorbidity combinations, it was observed that
Pneumonia-Hypertension, Pneumonia-Diabetes and Acute Respiratory Distress
Syndrome (ARDS)-Hypertension showed the most significant effects on COVID-19
mortality.
Conclusions: These results highlight patient cohorts most at risk of COVID-19
related severe morbidity and mortality which have implications for
prioritization of hospital resources
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Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression
Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment
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Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression
Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment
Proposing new variables for the identification of strategic groups in franchising
The identification of strategic groups in the Spanish franchising area is the
main aim of this study. The authors have added some new strategic variables (not
used before) to the study and have classified franchisors between sectors and
distribution strategy. The results reveal the existence of four perfectly differentiated
strategic groups (types of franchisors). One of the major implications of this study is
that the variables that build a strategic group vary depending on the respective sector the network operates in and its distribution strategy. This fact indicates that including sector and distribution strategy is absolutely necessary to achieve good classifications of franchisor type
Disparities and risks of sexually transmissible infections among men who have sex with men in China: a meta-analysis and data synthesis.
BACKGROUND: Sexually transmitted infections (STIs), including Hepatitis B and C virus, are emerging public health risks in China, especially among men who have sex with men (MSM). This study aims to assess the magnitude and risks of STIs among Chinese MSM. METHODS: Chinese and English peer-reviewed articles were searched in five electronic databases from January 2000 to February 2013. Pooled prevalence estimates for each STI infection were calculated using meta-analysis. Infection risks of STIs in MSM, HIV-positive MSM and male sex workers (MSW) were obtained. This review followed the PRISMA guidelines and was registered in PROSPERO. RESULTS: Eighty-eight articles (11 in English and 77 in Chinese) investigating 35,203 MSM in 28 provinces were included in this review. The prevalence levels of STIs among MSM were 6.3% (95% CI: 3.5-11.0%) for chlamydia, 1.5% (0.7-2.9%) for genital wart, 1.9% (1.3-2.7%) for gonorrhoea, 8.9% (7.8-10.2%) for hepatitis B (HBV), 1.2% (1.0-1.6%) for hepatitis C (HCV), 66.3% (57.4-74.1%) for human papillomavirus (HPV), 10.6% (6.2-17.6%) for herpes simplex virus (HSV-2) and 4.3% (3.2-5.8%) for Ureaplasma urealyticum. HIV-positive MSM have consistently higher odds of all these infections than the broader MSM population. As a subgroup of MSM, MSW were 2.5 (1.4-4.7), 5.7 (2.7-12.3), and 2.2 (1.4-3.7) times more likely to be infected with chlamydia, gonorrhoea and HCV than the broader MSM population, respectively. CONCLUSION: Prevalence levels of STIs among MSW were significantly higher than the broader MSM population. Co-infection of HIV and STIs were prevalent among Chinese MSM. Integration of HIV and STIs healthcare and surveillance systems is essential in providing effective HIV/STIs preventive measures and treatments. TRIAL REGISTRATION: PROSPERO NO: CRD42013003721
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
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