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Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer ’s disease on routinely acquired T1-weighted imaging-based brain network
ConclusionThe proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - March 6, 2024 Category: Information Technology Source Type: research

Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs
AbstractDiagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial –temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electr...
Source: Health Information Science and Systems - March 2, 2024 Category: Information Technology Source Type: research

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation
In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and diff...
Source: Health Information Science and Systems - February 29, 2024 Category: Information Technology Source Type: research

Hierarchical classification of early microscopic lung nodule based on cascade network
ConclusionsDifferent from the existing methods for categorizing the benign and malignant nature of lung nodules, the approach presented in this paper can classify lung nodules into 6 categories more accurately. At the same time, This paper proposes a rapid, precise, and dependable approach for classifying six distinct categories of lung nodules, which increases the accuracy categorization compared with the traditional multivariate categorization method. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - February 23, 2024 Category: Information Technology Source Type: research

Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification
ConclusionThe proposed AFFB-CAN method improves the performance of MI classification. In addition, our study confirms previous findings that motor imagery is mainly associated withµ andβ rhythms. Moreover, we also find thatγ rhythms also play an important role in MI classification. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - February 23, 2024 Category: Information Technology Source Type: research

Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration
AbstractCancer is one of the most deadly diseases in the world. Accurate cancer subtype classification is critical for patient diagnosis, treatment, and prognosis. Ever-increasing multi-omics data describes the characteristics of the patients from different views and serves as complementary information to promote cancer subtype identification. However, omics data generally have different distributions and high dimensions. How to effectively integrate multiple omics data to classify cancer subtypes accurately is a challenge for researchers. This work proposes a method integrating multi-omics data based on supervised graph c...
Source: Health Information Science and Systems - February 23, 2024 Category: Information Technology Source Type: research

Enhanced performance of EEG-based brain –computer interfaces by joint sample and feature importance assessment
AbstractElectroencephalograph (EEG) has been a reliable data source for building brain –computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG sample s to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end...
Source: Health Information Science and Systems - February 17, 2024 Category: Information Technology Source Type: research

A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery
ConclusionThe findings computationally establish the facts about the effects of birth weight-nutritional status pairs on disease development and disease recovery. As a social implication, this study can spread awareness about the importance of birth weight and nutritional status. The outcome can be helpful for the concerned authority in making decisions on healthcare cost and expenditure. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - February 17, 2024 Category: Information Technology Source Type: research

MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis
AbstractCardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and rhythm. However, the diagnostic utility of ECG signals is often hindered by various types of noise interference. In this work, we propose a novel filter based on a multi-engine evolution framework named MEAs-Filter to address this issue. Our approach eliminates the need for predefined dimensions and allows adaptation to diverse ECG morphologies. By leveraging state-of-the-art optimization algorithms as evolution engine and incorporating prior information inputs from classica...
Source: Health Information Science and Systems - January 23, 2024 Category: Information Technology Source Type: research

Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning framework
In this study, we propose a Dual-branch Attention Learning (DBAL) convolutional neural network framework to enhance thyroid nodule detection by capturing contextual information. Leveraging jigsaw puzzles as a pretext task during network training, we improve the network ’s generalization ability with limited data. Our framework effectively captures intrinsic features in a global-to-local manner. Experimental results involve self-supervised pre-training on unlabeled ultrasound images and fine-tuning using 1216 clinical ultrasound images from a collaborating hospit al. DBAL achieves accurate discrimination of thyroid nodule...
Source: Health Information Science and Systems - January 17, 2024 Category: Information Technology Source Type: research

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine
AbstractProstate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer ’s initiation and progression. However, single-omic studies usu...
Source: Health Information Science and Systems - December 18, 2023 Category: Information Technology Source Type: research

LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images
AbstractLiver cancer is the leading cause of mortality in the world. Over the years, researchers have spent much effort in developing computer-aided techniques to improve clinicians ’ diagnosis efficiency and precision, aiming at helping patients with liver cancer to take treatment early. Recently, attention mechanisms can enhance the representational power of convolutional neural networks (CNNs), which have been widely used in medical image analysis. In this paper, we propos e a novel architectural unit, local cross-channel recalibration (LCR) module, dynamically adjusting the relative importance of intermediate feature...
Source: Health Information Science and Systems - December 11, 2023 Category: Information Technology Source Type: research

Self-supervised neural network-based endoscopic monocular 3D reconstruction method
AbstractBased on deep learning, monocular visual 3D reconstruction methods have been applied in various conventional fields. In the aspect of medical endoscopic imaging, due to the difficulty in obtaining real information, self-supervised deep learning has always been a focus of research. However, current research on endoscopic 3D reconstruction is mainly conducted in laboratory environments, lacking experience in dealing with complex clinical surgical environments. In this work, we use an optical flow-based neural network to address the problem of inconsistent brightness between frames. Additionally, attention modules and...
Source: Health Information Science and Systems - December 11, 2023 Category: Information Technology Source Type: research

Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning
AbstractCardiovascular disease (CVDs) has become one of the leading causes of death, posing a significant threat to human life. The development of reliable Artificial Intelligence (AI) assisted diagnosis algorithms for cardiac sounds is of great significance for early detection and treatment of CVDs. However, there is scarce research in this field. Existing research mainly faces three major challenges: (1) They mainly limited to murmur classification and cannot achieve murmur grading, but attempting both classification and grading may lead to negative effects between different multi-tasks. (2) They mostly pay attention to ...
Source: Health Information Science and Systems - December 1, 2023 Category: Information Technology Source Type: research

Viewpoint-invariant exercise repetition counting
AbstractCounting the repetition of human exercise and physical rehabilitation is common in rehabilitation and exercise training. The existing vision-based repetition counting methods less emphasize the concurrent motions in the same video, and counting skeleton in different view angles. This work analyzed the spectrogram of the pose estimation cosine similarity to count the repetition. Besides the public datasets. This work also collected exercise videos from 11 adults to verify that the proposed method can handle concurrent motion and different view angles. The presented method was validated on the University of Idaho Phy...
Source: Health Information Science and Systems - December 1, 2023 Category: Information Technology Source Type: research