A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS.

This investigation sought to discern the risk factors for atrial fibrillation within Shanghai's Chongming District, analyzing data from 678 patients treated at a tertiary hospital in Chongming District, Shanghai, from 2020 to 2023, collecting information on season, C-reactive protein, hypertension, platelets, and other relevant indicators. The researchers introduced a novel dual feature-selection methodology, combining hierarchical clustering with Fisher scores (HC-MFS), to benchmark against four established methods. Through the training of five classification models on a designated dataset, the most effective model was chosen for method performance evaluation, with validation confirmed by test set scores. Impressively, the HC-MFS approach achieved the highest accuracy and the lowest root mean square error in the classification model, at 0.9118 and 0.2970, respectively. This provides a higher performance compared to existing methods, thanks to the combination and interaction of the two methods, which improves the quality of the feature subset. The research identified seasonal changes that were strongly associated with atrial fibrillation (pr = 0.31, FS = 0.11, and DCFS = 0.33, ranked first in terms of correlation); LDL cholesterol, total cholesterol, C-reactive protein, and platelet count, which are associated with inflammatory response and coronary heart disease, also indirectly contribute to atrial fibrillation and are risk factors for AF. Conclusively, this study advocates that machine-learning models can significantly aid clinicians in diagnosing individuals predisposed to atrial fibrillation, which shows a strong correlation with both pathological and climatic elements, especially seasonal variations, in the Chongming District.

Spatial attention-based implicit neural representation for arbitrary reduction of MRI slice spacing.

Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution. Super-resolution technique can enhance the through-plane resolution of MR images to facilitate downstream visualization and computer-aided diagnosis. However, most existing works train the super-resolution network at a fixed scaling factor, which is not friendly to clinical scenes of varying inter-slice spacing in MR scanning. Inspired by the recent progress in implicit neural representation, we propose a Spatial Attention-based Implicit Neural Representation (SA-INR) network for arbitrary reduction of MR inter-slice spacing. The SA-INR aims to represent an MR image as a continuous implicit function of 3D coordinates. In this way, the SA-INR can reconstruct the MR image with arbitrary inter-slice spacing by continuously sampling the coordinates in 3D space. In particular, a local-aware spatial attention operation is introduced to model nearby voxels and their affinity more accurately in a larger receptive field. Meanwhile, to improve the computational efficiency, a gradient-guided gating mask is proposed for applying the local-aware spatial attention to selected areas only. We evaluate our method on the public HCP-1200 dataset and the clinical knee MR dataset to demonstrate its superiority over other existing methods.

A survey of data element perspective: Application of artificial intelligence in health big data.

Artificial intelligence (AI) based on the perspective of data elements is widely used in the healthcare informatics domain. Large amounts of clinical data from electronic medical records (EMRs), electronic health records (EHRs), and electroencephalography records (EEGs) have been generated and collected at an unprecedented speed and scale. For instance, the new generation of wearable technologies enables easy-collecting peoples' daily health data such as blood pressure, blood glucose, and physiological data, as well as the application of EHRs documenting large amounts of patient data. The cost of acquiring and processing health big data is expected to reduce dramatically with the help of AI technologies and open-source big data platforms such as Hadoop and Spark. The application of AI technologies in health big data presents new opportunities to discover the relationship among living habits, sports, inheritances, diseases, symptoms, and drugs. Meanwhile, with the development of fast-growing AI technologies, many promising methodologies are proposed in the healthcare field recently. In this paper, we review and discuss the application of machine learning (ML) methods in health big data in two major aspects: (1) Special features of health big data including multimodal, incompletion, time validation, redundancy, and privacy. (2) ML methodologies in the healthcare field including classification, regression, clustering, and association. Furthermore, we review the recent progress and breakthroughs of automatic diagnosis in health big data and summarize the challenges, gaps, and opportunities to improve and advance automatic diagnosis in the health big data field.

A novel general-purpose hybrid model for time series forecasting.

Realizing the accurate prediction of data flow is an important and challenging problem in industrial automation. However, due to the diversity of data types, it is difficult for traditional time series prediction models to have good prediction effects on different types of data. To improve the versatility and accuracy of the model, this paper proposes a novel hybrid time-series prediction model based on recursive empirical mode decomposition (REMD) and long short-term memory (LSTM). In REMD-LSTM, we first propose a new REMD to overcome the marginal effects and mode confusion problems in traditional decomposition methods. Then use REMD to decompose the data stream into multiple in intrinsic modal functions (IMF). After that, LSTM is used to predict each IMF subsequence separately and obtain the corresponding prediction results. Finally, the true prediction value of the input data is obtained by accumulating the prediction results of all IMF subsequences. The final experimental results show that the prediction accuracy of our proposed model is improved by more than 20% compared with the LSTM algorithm. In addition, the model has the highest prediction accuracy on all different types of data sets. This fully shows the model proposed in this paper has a greater advantage in prediction accuracy and versatility than the state-of-the-art models. The data used in the experiment can be downloaded from this website: https://github.com/Yang-Yun726/REMD-LSTM.