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General General

A cross-sectional study: a breathomics based pulmonary tuberculosis detection method.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection.

METHOD : Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients.

RESULTS : The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961.

CONCLUSIONS : The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

Fu Liang, Wang Lei, Wang Haibo, Yang Min, Yang Qianting, Lin Yi, Guan Shanyi, Deng Yongcong, Liu Lei, Li Qingyun, He Mengqi, Zhang Peize, Chen Haibin, Deng Guofang

2023-Mar-10

Breathomics, Machine learning, Pulmonary tuberculosis, Volatile organic compounds

Public Health Public Health

Moderation effects of serotype on dengue severity across pregnancy status in Mexico.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Pregnancy increases a woman's risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico.

METHOD : Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status.

RESULTS : Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype.

CONCLUSION : The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico.

Annan Esther, Nguyen Uyen-Sa D T, Treviño Jesús, Wan Yaacob Wan Fairos, Mangla Sherry, Pathak Ashok Kumar, Nandy Rajesh, Haque Ubydul

2023-Mar-10

Dengue, Mexico, Pregnancy, Reproductive age, Serotype, Severe, Severity, Women

General General

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.

In Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics

The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improve patient outcomes after stroke in selected patients; however, significant gaps remain in our ability to select patients, predict complications, and understand outcomes. Big data and the computational methods needed to analyze it can address these gaps. For example, automated analysis of neuroimaging to estimate the volume of brain tissue that is ischemic and salvageable can help triage patients for acute interventions. Data-intensive computational techniques can perform complex risk calculations that are too cumbersome to be completed by humans, resulting in more accurate and timely prediction of which patients require increased vigilance for adverse events such as treatment complications. To handle the accumulation of complex medical data, a variety of advanced computational techniques referred to as machine learning and artificial intelligence now routinely complement traditional statistical inference. In this narrative review, we explore data-intensive techniques in stroke research, how it has informed the management of stroke patients, and how current work could shape clinical practice in the future.

Liu Yuzhe, Luo Yuan, Naidech Andrew M

2023-Mar-10

Artificial intelligence, Big data, Deep learning, Machine learning, Stroke complications, Stroke management

Public Health Public Health

A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.

In Scientific reports ; h5-index 158.0

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.

Hammad Muhammed S, Ghoneim Vidan F, Mabrouk Mai S, Al-Atabany Walid I

2023-Mar-10

General General

Predicting hospital emergency department visits accurately: A systematic review.

In The International journal of health planning and management

OBJECTIVES : The emergency department (ED) is a very important healthcare entrance point, known for its challenging organisation and management due to demand unpredictability. An accurate forecast system of ED visits is crucial to the implementation of better management strategies that optimise resources utilization, reduce costs and improve public confidence. The aim of this review is to investigate the different factors that affect the ED visits forecasting outcomes, in particular the predictive variables and type of models applied.

METHODS : A systematic search was conducted in PubMed, Web of Science and Scopus. The review methodology followed the PRISMA statement guidelines.

RESULTS : Seven studies were selected, all exploring predictive models to forecast ED daily visits for general care. MAPE and RMAE were used to measure models' accuracy. All models displayed good accuracy, with errors below 10%.

CONCLUSIONS : Model selection and accuracy was found to be particularly sensitive to the ED dimension. While ARIMA-based and other linear models have good performance for short-time forecast, some machine learning methods proved to be more stable when forecasting multiple horizons. The inclusion of exogenous variables was found to be advantageous only in bigger EDs.

Silva Eduardo, Pereira Margarida F, Vieira Joana T, Ferreira-Coimbra João, Henriques Mariana, Rodrigues Nuno F

2023-Mar-10

emergency department, forecasting, hospital, predictive models, resource management, visits

General General

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next.

In Heliyon

As a form of clean energy, nuclear energy has unique advantages compared to other energy sources in the present era, where low-carbon policies are being widely advocated. The exponential growth of artificial intelligence (AI) technology in recent decades has resulted in new opportunities and challenges in terms of improving the safety and economics of nuclear reactors. This study briefly introduces modern AI algorithms such as machine learning, deep learning, and evolutionary computing. Furthermore, several studies on the use of AI techniques for nuclear reactor design optimization as well as operation and maintenance (O&M) are reviewed and discussed. The existing obstacles that prevent the further fusion of AI and nuclear reactor technologies so that they can be scaled to real-world problems are classified into two categories: (1) data issues: insufficient experimental data increases the possibility of data distribution drift and data imbalance; (2) black-box dilemma: methods such as deep learning have poor interpretability. Finally, this study proposes two directions for the future fusion of AI and nuclear reactor technologies: (1) better integration of domain knowledge with data-driven approaches to reduce the high demand for data and improve the model performance and robustness; (2) promoting the use of explainable artificial intelligence (XAI) technologies to enhance the transparency and reliability of the model. In addition, causal learning warrants further attention owing to its inherent ability to solve out-of-distribution generalization (OODG) problems.

Huang Qingyu, Peng Shinian, Deng Jian, Zeng Hui, Zhang Zhuo, Liu Yu, Yuan Peng

2023-Mar

Artificial intelligence, Causal learning, Nuclear reactors, SciML, XAI