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Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents.

Hay que agregar la siguiente descripción: "This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the [JOURNAL TITLE] [date of publication] [copyright Taylor & Francis], available online at: .", The electrocardiogram (ECG) is often acquired during magnetic resonance imaging (MRI), but its analysis is restricted by the presence of a strong artefact, called magnetohydrodynamic (MHD) effect. MHD effect is induced by the flow of electrically charged particles in the blood perpendicular to the static magnetic field, which creates a potential of the order of magnitude of the ECG and temporally coincident with the repolarisation period. In this study, a new MHD model is proposed by using MRI-based 4D blood flow measurements made across the aortic arch. The model is extended to several cardiac cycles to allow the simulation of a realistic ECG acquisition during MRI examination and the quality assessment of MHD suppression techniques. A comparison of two existing models, based, respectively, on an analytical solution and on a numerical method-based solution of the fluids dynamics problem, is made with the proposed model and with an estimate of the MHD voltage observed during a real MRI scan. Results indicate a moderate agreement between the proposed model and the estimated MHD model for most leads, with an average correlation factor of 0.47. However, the results demonstrate that the proposed model provides a closer approximation to the observed MHD effects and a better depiction of the complexity of the MHD effect compared with the previously published models, with an improved correlation (+5%), coefficient of determination (+1%) and fraction of energy (+22%) compared with the best previous model. The source code will be made freely available under an open source licence to facilitate collaboration and allow more rapid development of more accurate models of the MHD effect.

Hay que agregar la siguiente descripción: "This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the [JOURNAL TITLE] [date of publication] [copyright Taylor & Francis], available online at: .", The electrocardiogram (ECG) is often acquired during magnetic resonance imaging (MRI), but its analysis is restricted by the presence of a strong artefact, called magnetohydrodynamic (MHD) effect. MHD effect is induced by the flow of electrically charged particles in the blood perpendicular to the static magnetic field, which creates a potential of the order of magnitude of the ECG and temporally coincident with the repolarisation period. In this study, a new MHD model is proposed by using MRI-based 4D blood flow measurements made across the aortic arch. The model is extended to several cardiac cycles to allow the simulation of a realistic ECG acquisition during MRI examination and the quality assessment of MHD suppression techniques. A comparison of two existing models, based, respectively, on an analytical solution and on a numerical method-based solution of the fluids dynamics problem, is made with the proposed model and with an estimate of the MHD voltage observed during a real MRI scan. Results indicate a moderate agreement between the proposed model and the estimated MHD model for most leads, with an average correlation factor of 0.47. However, the results demonstrate that the proposed model provides a closer approximation to the observed MHD effects and a better depiction of the complexity of the MHD effect compared with the previously published models, with an improved correlation (+5%), coefficient of determination (+1%) and fraction of energy (+22%) compared with the best previous model. The source code will be made freely available under an open source licence to facilitate collaboration and allow more rapid development of more accurate models of the MHD effect.