Health Information Science and Systems最新文献_第6页

EAPR: explainable and augmented patient representation learning for disease prediction EAPR:用于疾病预测的可解释和增强的患者表征学习

3区医学

Health Information Science and Systems Pub Date : 2023-11-14 DOI: 10.1007/s13755-023-00256-5

Jiancheng Zhang, Yonghui Xu, Bicui Ye, Yibowen Zhao, Xiaofang Sun, Qi Meng, Yang Zhang, Lizhen Cui

引用次数: 0

ADHD-KG: a knowledge graph of attention deficit hyperactivity disorder ADHD-KG:注意缺陷多动障碍的知识图谱

3区医学

Health Information Science and Systems Pub Date : 2023-11-11 DOI: 10.1007/s13755-023-00253-8

Emmanuel Papadakis, George Baryannis, Sotiris Batsakis, Marios Adamou, Zhisheng Huang, Grigoris Antoniou

{"title":"ADHD-KG: a knowledge graph of attention deficit hyperactivity disorder","authors":"Emmanuel Papadakis, George Baryannis, Sotiris Batsakis, Marios Adamou, Zhisheng Huang, Grigoris Antoniou","doi":"10.1007/s13755-023-00253-8","DOIUrl":"https://doi.org/10.1007/s13755-023-00253-8","url":null,"abstract":"Abstract Purpose Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition that affects human behaviour and can interfere with daily activities and relationships. Medication or medical information about ADHD can be found in several data sources on the Web. Such distribution of knowledge raises notable obstacles since researchers and clinicians must manually combine various sources to deeply explore aspects of ADHD. Knowledge graphs have been widely used in medical applications due to their data integration capabilities, offering rich data stores of information built from heterogeneous sources; however, general purpose knowledge graphs cannot represent knowledge in sufficient detail, thus there is an increasing interest in domain-specific knowledge graphs. Methods In this work we propose a Knowledge Graph of ADHD. In particular, we introduce an automated procedure enabling the construction of a knowledge graph that covers knowledge from a wide range of data sources primarily focusing on adult ADHD. These include relevant literature and clinical trials, prescribed medication and their known side-effects. Data integration between these data sources is accomplished by employing a suite of information linking procedures, which aim to connect resources by relating them to common concepts found in medical thesauri. Results The usability and appropriateness of the developed knowledge graph is evaluated through a series of use cases that illustrate its ability to enhance and accelerate information retrieval. Conclusion The Knowledge Graph of ADHD can provide valuable assistance to researchers and clinicians in the research, training, diagnostic and treatment processes for ADHD.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"13 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135087208","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}

引用次数: 0

Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing. 基于高阶相互作用和样本分布再平衡的肝纤维化MR图像分类。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2023-11-08 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00255-6

Ling Zhang, Zhennan Xiao, Wenchao Jiang, Chengbin Luo, Ming Ye, Guanghui Yue, Zhiyuan Chen, Shuman Ouyang, Yupin Liu

{"title":"Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing.","authors":"Ling Zhang, Zhennan Xiao, Wenchao Jiang, Chengbin Luo, Ming Ye, Guanghui Yue, Zhiyuan Chen, Shuman Ouyang, Yupin Liu","doi":"10.1007/s13755-023-00255-6","DOIUrl":"10.1007/s13755-023-00255-6","url":null,"abstract":"The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"51"},"PeriodicalIF":4.7,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89719974","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}

引用次数: 0

Predicting drug-drug interactions based on multi-view and multichannel attention deep learning. 基于多视角和多渠道注意力深度学习预测药物相互作用。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2023-11-06 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00250-x

Liyu Huang, Qingfeng Chen, Wei Lan

{"title":"Predicting drug-drug interactions based on multi-view and multichannel attention deep learning.","authors":"Liyu Huang, Qingfeng Chen, Wei Lan","doi":"10.1007/s13755-023-00250-x","DOIUrl":"10.1007/s13755-023-00250-x","url":null,"abstract":"Predicting drug-drug interactions (DDIs) has become a major concern in the drug research field because it helps explore the pharmacological function of drugs and enables the development of new therapeutic drugs. Existing prediction methods simply integrate multiple drug attributes or perform tasks on a biomedical knowledge graph (KG). Though effective, few methods can fully utilize multi-source drug data information. In this paper, a multi-view and multichannel attention deep learning (MMADL) model is proposed, which not only extracts rich drug features containing both drug attributes and drug-related entity information from multi-source databases, but also considers the consistency and complementarity of different drug feature representation learning approaches to improve the effectiveness and accuracy of DDI prediction. A single-layer perceptron encoder is applied to encode multi-source drug information to obtain multi-view drug representation vectors in the same linear space. Then, the multichannel attention mechanism is introduced to obtain the attention weight by adaptively learning the importance of drug features according to their contributions to DDI prediction. Further, the representation vectors of multi-view drug pairs with attention weights are used as inputs of the deep neural network to predict potential DDI. The accuracy and precision-recall curves of MMADL are 93.05 and 95.94, respectively. The results indicate that the proposed method outperforms other state-of-the-art methods.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"50"},"PeriodicalIF":4.7,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71522917","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}

引用次数: 0

Thyroidkeeper: a healthcare management system for patients with thyroid diseases. 甲状腺守护者：甲状腺疾病患者的医疗管理系统。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2023-10-17 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00251-w

Jing Zhang, Jianhua Li, Yi Zhu, Yu Fu, Lixia Chen

{"title":"Thyroidkeeper: a healthcare management system for patients with thyroid diseases.","authors":"Jing Zhang, Jianhua Li, Yi Zhu, Yu Fu, Lixia Chen","doi":"10.1007/s13755-023-00251-w","DOIUrl":"10.1007/s13755-023-00251-w","url":null,"abstract":"Thyroid diseases, especially thyroid tumors, have a huge population in China. The postoperative patients, under China's incomplete tertiary diagnosis and treatment system, will frequently go to tertiary hospitals for follow-up and medication adjustment, resulting in heavy burdens on both specialists and patients. To help postoperative patients recover better against the above adverse conditions, a novel mobile application ThyroidKeeper is proposed as a collaborative AI-based platform that benefits both patients and doctors. In addition to routine health records and management functions, ThyroidKeeper has achieved several innovative points. First, it can automatically adjust medication dosage for patients during their rehabilitation based on their medical history, laboratory indicators, physical health status, and current medication. Second, it can comprehensively predict the possible complications based on the patient's health status and the health status of similar groups utilizing graph neural networks. Finally, the employing of graph neural network models can improve the efficiency of online communication between doctors and patients, help doctors obtain medical information for patients more quickly and precisely, and make more accurate diagnoses. The preliminary evaluation in both laboratory and real-world environments shows the advantages of the proposed ThyroidKeeper system.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"49"},"PeriodicalIF":4.7,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683477","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}

引用次数: 0

Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan. 联合机器学习预测危重患者急性肾损伤：台湾的一项多中心研究。

IF 6 3区医学

Health Information Science and Systems Pub Date : 2023-10-09 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00248-5

Chun-Te Huang, Tsai-Jung Wang, Li-Kuo Kuo, Ming-Ju Tsai, Cong-Tat Cia, Dung-Hung Chiang, Po-Jen Chang, Inn-Wen Chong, Yi-Shan Tsai, Yuan-Chia Chu, Chia-Jen Liu, Cheng-Hsu Chen, Kai-Chih Pai, Chieh-Liang Wu

{"title":"Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.","authors":"Chun-Te Huang, Tsai-Jung Wang, Li-Kuo Kuo, Ming-Ju Tsai, Cong-Tat Cia, Dung-Hung Chiang, Po-Jen Chang, Inn-Wen Chong, Yi-Shan Tsai, Yuan-Chia Chu, Chia-Jen Liu, Cheng-Hsu Chen, Kai-Chih Pai, Chieh-Liang Wu","doi":"10.1007/s13755-023-00248-5","DOIUrl":"10.1007/s13755-023-00248-5","url":null,"abstract":"Purpose: To address the contentious data sharing across hospitals, this study adopted a novel approach, federated learning (FL), to establish an aggregate model for acute kidney injury (AKI) prediction in critically ill patients in Taiwan.Methods: This study used data from the Critical Care Database of Taichung Veterans General Hospital (TCVGH) from 2015 to 2020 and electrical medical records of the intensive care units (ICUs) between 2018 and 2020 of four referral centers in different areas across Taiwan. AKI prediction models were trained and validated thereupon. An FL-based prediction model across hospitals was then established.Results: The study included 16,732 ICU admissions from the TCVGH and 38,424 ICU admissions from the other four hospitals. The complete model with 60 features and the parsimonious model with 21 features demonstrated comparable accuracies using extreme gradient boosting, neural network (NN), and random forest, with an area under the receiver-operating characteristic (AUROC) curve of approximately 0.90. The Shapley Additive Explanations plot demonstrated that the selected features were the key clinical components of AKI for critically ill patients. The AUROC curve of the established parsimonious model for external validation at the four hospitals ranged from 0.760 to 0.865. NN-based FL slightly improved the model performance at the four centers.Conclusion: A reliable prediction model for AKI in ICU patients was developed with a lead time of 24 h, and it performed better when the novel FL platform across hospitals was implemented.Supplementary information: The online version contains supplementary material available at 10.1007/s13755-023-00248-5.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"48"},"PeriodicalIF":6.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41215739","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}

引用次数: 0

A new segment method for pulmonary artery and vein. 一种新的肺动静脉分割方法。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2023-10-06 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00245-8

Qinghua Zhou, Wenjun Tan, Qingya Li, Baoting Li, Luyu Zhou, Xin Liu, Jinzhu Yang, Dazhe Zhao

引用次数: 0

M-MSSEU: source-free domain adaptation for multi-modal stroke lesion segmentation using shadowed sets and evidential uncertainty. M-MSSEU：使用阴影集和证据不确定性进行多模式中风病变分割的无源域自适应。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2023-09-28 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00247-6

Zhicheng Wang, Hongqing Zhu, Bingcang Huang, Ziying Wang, Weiping Lu, Ning Chen, Ying Wang

{"title":"M-MSSEU: source-free domain adaptation for multi-modal stroke lesion segmentation using shadowed sets and evidential uncertainty.","authors":"Zhicheng Wang, Hongqing Zhu, Bingcang Huang, Ziying Wang, Weiping Lu, Ning Chen, Ying Wang","doi":"10.1007/s13755-023-00247-6","DOIUrl":"10.1007/s13755-023-00247-6","url":null,"abstract":"Due to the unavailability of source domain data encountered in unsupervised domain adaptation, there has been an increasing number of studies on source-free domain adaptation (SFDA) in recent years. To better solve the SFDA problem and effectively leverage the multi-modal information in medical images, this paper presents a novel SFDA method for multi-modal stroke lesion segmentation in which evidential deep learning instead of convolutional neural network. Specifically, for multi-modal stroke images, we design a multi-modal opinion fusion module which uses Dempster-Shafer evidence theory for decision fusion of different modalities. Besides, for the SFDA problem, we use the pseudo label learning method, which obtains pseudo labels from the pre-trained source model to perform the adaptation process. To solve the unreliability of pseudo label caused by domain shift, we propose a pseudo label filtering scheme using shadowed sets theory and a pseudo label refining scheme using evidential uncertainty. These two schemes can automatically extract unreliable parts in pseudo labels and jointly improve the quality of pseudo labels with low computational costs. Experiments on two multi-modal stroke lesion datasets demonstrate the superiority of our method over other state-of-the-art SFDA methods.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"46"},"PeriodicalIF":4.7,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41162618","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}

引用次数: 0

Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification. Medimatrix：用于类风湿性关节炎诊断的灰度图像的创新预训练彻底改变了医学图像分类。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2023-09-26 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00246-7

Linchen Liu, Yiyang Zhang, Le Sun

{"title":"Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification.","authors":"Linchen Liu, Yiyang Zhang, Le Sun","doi":"10.1007/s13755-023-00246-7","DOIUrl":"10.1007/s13755-023-00246-7","url":null,"abstract":"Efficient and accurate medical image classification (MIC) methods face two major challenges: (1) high similarity between images of different disease classes; and (2) generating large medical image datasets for training deep neural networks is challenging due to privacy restrictions and the need for expert ground truth annotations. In this paper, we introduce a novel deep learning method called pre-training grayscale images with supervised learning for MIC (MediMatrix). Instead of pre-training on color ImageNet, our approach uses MediMatrix on grayscale ImageNet. To improve the performance of the network, we introduce ShuffleAttention (SA), a self-attention mechanism. By combining SA with the multiple residual structure (ResSA block) and replacing short-cut connections with dense residual connections between corresponding layers (densepath), our network can dynamically adjust channel attention weights and receive image inputs of different sizes, resulting in improved feature representation and better discrimination of similarities between different categories. MediMatrix effectively classifies X-ray images of rheumatoid arthritis (RA), enabling efficient screening without the need for expert analysis or invasive testing. Through extensive experiments, we demonstrate the superiority of MediMatrix over state-of-the-art methods and that color is not critical for rich natural image classification. Our results highlight the potential of computer-aided diagnosis combined with MediMatrix as a valuable screening tool for early detection and intervention in RA.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"44"},"PeriodicalIF":4.7,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147866","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}

引用次数: 0

An error-bounded median filter for correcting ECG baseline wander. 一种用于校正ECG基线漂移的误差有界中值滤波器。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2023-09-26 eCollection Date: 2023-12-01 DOI: 10.1007/s13755-023-00235-w

Huanyu Zhao, Tongliang Li, Jian Yang, Chaoyi Pang

引用次数: 0