Health Information Science and Systems最新文献

{"title":"Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation.","authors":"Kalliopi-Maria Stathopoulou, Spiros Georgakopoulos, Sotiris Tasoulis, Vassilis P Plagianakos","doi":"10.1007/s13755-023-00265-4","DOIUrl":"10.1007/s13755-023-00265-4","url":null,"abstract":"<p><p>Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. 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 differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"14"},"PeriodicalIF":4.7,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140022947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced performance of EEG-based brain-computer interfaces by joint sample and feature importance assessment.","authors":"Xing Li, Yikai Zhang, Yong Peng, Wanzeng Kong","doi":"10.1007/s13755-024-00271-0","DOIUrl":"10.1007/s13755-024-00271-0","url":null,"abstract":"<p><p>Electroencephalograph (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 samples 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, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"9"},"PeriodicalIF":4.7,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10874355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139906580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery.","authors":"Zakir Hussain, Malaya Dutta Borah","doi":"10.1007/s13755-024-00272-z","DOIUrl":"10.1007/s13755-024-00272-z","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this work is to analyse the combined impacts of birth weight and nutritional status on development and recovery of various types of diseases. This work aims to computationally establish the facts about the effects of individual birth weight-nutritional status pairs on disease development and disease recovery.</p><p><strong>Methods: </strong>This work designs a computational model to analyze the impact of birth weight-nutritional status pairs on disease development and disease recovery. Our model works in two phases. The first phase finds the best machine learning model to predict birth weight from \"Child Birth Weight Dataset\" available at IEEE Dataport (https://dx.doi.org/10.21227/dvd4-3232). The second phase combines the predicted birth weight labels with nutritional status labels and establishes the effects using differential equations.</p><p><strong>Results: </strong>The experimental results find Gradient boosting (GB) to work the best with Information gain (IGT) and Support Vector Machine (SVM) with Chi-square test (CST) for predicting the birth weights. The simulated results establish that \"normal birth weight and normal nutritional status\" is the best pair for resisting disease development as well as enhancing disease recovery. The results also depict that \"low birth weight and malnutrition\" is the worst pair for disease development while \"high birth weight and malnutrition\" is the worst combination for disease recovery.</p><p><strong>Conclusion: </strong>The 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.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"10"},"PeriodicalIF":4.7,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10874357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139906641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis.","authors":"Fangfang Zhu, Ji Ding, Xiang Li, Yuer Lu, Xiao Liu, Frank Jiang, Qi Zhao, Honghong Su, Jianwei Shuai","doi":"10.1007/s13755-023-00268-1","DOIUrl":"10.1007/s13755-023-00268-1","url":null,"abstract":"<p><p>Cardiovascular 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 classical filters, MEAs-Filter achieves superior performance while minimizing order. We evaluate the effectiveness of MEAs-Filter on a real ECG database and compare it against commonly used filters such as the Butterworth, Chebyshev filters, and evolution algorithm-based (EA-based) filters. The experimental results indicate that MEAs-Filter outperforms other filters by achieving a reduction of approximately 30% to 60% in terms of the loss function compared to the other algorithms. In denoising experiments conducted on ECG waveforms across various scenarios, MEAs-Filter demonstrates an improvement of approximately 20% in signal-to-noise (<i>SNR</i>) ratio and a 9% improvement in correlation. Moreover, it does not exhibit higher losses of the R-wave compared to other filters. These findings highlight the potential of MEAs-Filter as a valuable tool for high-fidelity extraction of ECG signals, enabling accurate diagnosis in the field of cardiovascular diseases.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"8"},"PeriodicalIF":4.7,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10805910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139565129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.","authors":"Shumin Ren, Jiakun Li, Julián Dorado, Alejandro Sierra, Humbert González-Díaz, Aliuska Duardo, Bairong Shen","doi":"10.1007/s13755-023-00264-5","DOIUrl":"10.1007/s13755-023-00264-5","url":null,"abstract":"<p><p>Prostate 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 usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"6"},"PeriodicalIF":4.7,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10728428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138832216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images","authors":"Qiang Fang, Yue Yang, Hao Wang, Hanxi Sun, Jiangming Chen, Zixiang Chen, Tian Pu, Xiaoqing Zhang, Fubao Liu","doi":"10.1007/s13755-023-00263-6","DOIUrl":"https://doi.org/10.1007/s13755-023-00263-6","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"10 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138584471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised neural network-based endoscopic monocular 3D reconstruction method","authors":"Ziming Zhang, Wenjun Tan, Yuhang Sun, Juntao Han, Zhe Wang, Hongsheng Xue, Ruoyu Wang","doi":"10.1007/s13755-023-00262-7","DOIUrl":"https://doi.org/10.1007/s13755-023-00262-7","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"4 8","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138584265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning.","authors":"Chenyang Xu, Xin Li, Xinyue Zhang, Ruilin Wu, Yuxi Zhou, Qinghao Zhao, Yong Zhang, Shijia Geng, Yue Gu, Shenda Hong","doi":"10.1007/s13755-023-00249-4","DOIUrl":"10.1007/s13755-023-00249-4","url":null,"abstract":"<p><p>Cardiovascular 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 unstructured cardiac sound modality and do not consider the structured demographic modality, as it is difficult to balance the influence of heterogeneous modalities. (3) Deep learning methods lack interpretability, which makes it challenging to apply them clinically. To tackle these challenges, we propose a method for cardiac murmur grading and cardiac risk analysis based on heterogeneous modality adaptive multi-task learning. Specifically, a Hierarchical Multi-Task learning-based cardiac murmur detection and grading method (HMT) is proposed to prevent negative interference between different tasks. In addition, a cardiac risk analysis method based on Heterogeneous Multi-modal feature impact Adaptation (HMA) is also proposed, which transforms unstructured modality into structured modality representation, and utilizes an adaptive mode weight learning mechanism to balance the impact between unstructured modality and structured modality, thus enhancing the performance of cardiac risk prediction. Finally, we propose a multi-task interpretability learning module that incorporates an important evaluation using random masks. This module utilizes SHAP graphs to visualize crucial murmur segments in cardiac sound and employs a multi-factor risk decoupling model based on nomograms. And then we gain insights into the cardiac disease risk in both pre-decoupled multi-modality and post-decoupled single-modality scenarios, thus providing a solid foundation for AI assisted cardiac murmur grading and risk analysis. Experimental results on a large real-world CirCor DigiScope PCG dataset demonstrate that the proposed method outperforms the state-of-the-art (SOTA) method in murmur detection, grading, and cardiac risk analysis, while also providing valuable diagnostic evidence.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"2"},"PeriodicalIF":4.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Viewpoint-invariant exercise repetition counting.","authors":"Yu Cheng Hsu, Tsougenis Efstratios, Kwok-Leung Tsui","doi":"10.1007/s13755-023-00258-3","DOIUrl":"10.1007/s13755-023-00258-3","url":null,"abstract":"<p><p>Counting 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 Physical Rehabilitation Movements Data Set (UI-PRMD) and MM-fit dataset. The overall mean absolute error (MAE) for MM-fit was 0.06 with off-by-one Accuracy (OBOA) of 0.94. As for the UI-PRMD dataset, MAE was 0.06 with OBOA 0.95. We have also tested the performance in various camera locations and concurrent motions with 57 skeleton time-series videos with an overall MAE of 0.07 and OBOA of 0.91. The proposed method provides a view-angle and motion agnostic concurrent motion counting. This method can potentially use in large-scale remote rehabilitation and exercise training with only one camera.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-023-00258-3.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"1"},"PeriodicalIF":4.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection.","authors":"Xiushun Zhao, Jing Guo, Zhaoshui He, Xiaobing Jiang, Haifang Lou, Depei Li","doi":"10.1007/s13755-023-00260-9","DOIUrl":"10.1007/s13755-023-00260-9","url":null,"abstract":"<p><p>As medical treatments continue to advance rapidly, minimally invasive surgery (MIS) has found extensive applications across various clinical procedures. Accurate identification of medical instruments plays a vital role in comprehending surgical situations and facilitating endoscopic image-guided surgical procedures. However, the endoscopic instrument detection poses a great challenge owing to the narrow operating space, with various interfering factors (e.g. smoke, blood, body fluids) and inevitable issues (e.g. mirror reflection, visual obstruction, illumination variation) in the surgery. To promote surgical efficiency and safety in MIS, this paper proposes a cross-layer aggregated attention detection network (CLAD-Net) for accurate and real-time detection of endoscopic instruments in complex surgical scenarios. We propose a cross-layer aggregation attention module to enhance the fusion of features and raise the effectiveness of lateral propagation of feature information. We propose a composite attention mechanism (CAM) to extract contextual information at different scales and model the importance of each channel in the feature map, mitigate the information loss due to feature fusion, and effectively solve the problem of inconsistent target size and low contrast in complex contexts. Moreover, the proposed feature refinement module (RM) enhances the network's ability to extract target edge and detail information by adaptively adjusting the feature weights to fuse different layers of features. The performance of CLAD-Net was evaluated using a public laparoscopic dataset Cholec80 and another set of neuroendoscopic dataset from Sun Yat-sen University Cancer Center. From both datasets and comparisons, CLAD-Net achieves the <math><mrow><mi>A</mi><msub><mi>P</mi><mrow><mn>0.5</mn></mrow></msub></mrow></math> of 98.9% and 98.6%, respectively, that is better than advanced detection networks. A video for the real-time detection is presented in the following link: https://github.com/A0268/video-demo.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"58"},"PeriodicalIF":4.7,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}