Health Information Science and Systems最新文献

{"title":"A multi-source heterogeneous medical data enhancement framework based on lakehouse.","authors":"Ming Sheng, Shuliang Wang, Yong Zhang, Rui Hao, Ye Liang, Yi Luo, Wenhan Yang, Jincheng Wang, Yinan Li, Wenkui Zheng, Wenyao Li","doi":"10.1007/s13755-024-00295-6","DOIUrl":"10.1007/s13755-024-00295-6","url":null,"abstract":"<p><p>Obtaining high-quality data sets from raw data is a key step before data exploration and analysis. Nowadays, in the medical domain, a large amount of data is in need of quality improvement before being used to analyze the health condition of patients. There have been many researches in data extraction, data cleaning and data imputation, respectively. However, there are seldom frameworks integrating with these three techniques, making the dataset suffer in accuracy, consistency and integrity. In this paper, a multi-source heterogeneous data enhancement framework based on a lakehouse MHDP is proposed, which includes three steps of data extraction, data cleaning and data imputation. In the data extraction step, a data fusion technique is offered to handle multi-modal and multi-source heterogeneous data. In the data cleaning step, we propose HoloCleanX, which provides a convenient interactive procedure. In the data imputation step, multiple imputation (MI) and the SOTA algorithm SAITS, are applied for different situations. We evaluate our framework via three tasks: clustering, classification and strategy prediction. The experimental results prove the effectiveness of our data enhancement framework.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"37"},"PeriodicalIF":4.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11226589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555685","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 transfer learning enabled approach for ocular disease detection and classification.","authors":"Mahmood Ul Hassan, Amin A Al-Awady, Naeem Ahmed, Muhammad Saeed, Jarallah Alqahtani, Ali Mousa Mohamed Alahmari, Muhammad Wasim Javed","doi":"10.1007/s13755-024-00293-8","DOIUrl":"10.1007/s13755-024-00293-8","url":null,"abstract":"<p><p>Ocular diseases pose significant challenges in timely diagnosis and effective treatment. Deep learning has emerged as a promising technique in medical image analysis, offering potential solutions for accurately detecting and classifying ocular diseases. In this research, we propose Ocular Net, a novel deep learning model for detecting and classifying ocular diseases, including Cataracts, Diabetic, Uveitis, and Glaucoma, using a large dataset of ocular images. The study utilized an image dataset comprising 6200 images of both eyes of patients. Specifically, 70% of these images (4000 images) were allocated for model training, while the remaining 30% (2200 images) were designated for testing purposes. The dataset contains images of five categories that include four diseases, and one normal category. The proposed model uses transfer learning, average pooling layers, Clipped Relu, Leaky Relu and various other layers to accurately detect the ocular diseases from images. Our approach involves training a novel Ocular Net model on diverse ocular images and evaluating its accuracy and performance metrics for disease detection. We also employ data augmentation techniques to improve model performance and mitigate overfitting. The proposed model is tested on different training and testing ratios with varied parameters. Additionally, we compare the performance of the Ocular Net with previous methods based on various evaluation parameters, assessing its potential for enhancing the accuracy and efficiency of ocular disease diagnosis. The results demonstrate that Ocular Net achieves 98.89% accuracy and 0.12% loss value in detecting and classifying ocular diseases by outperforming existing methods.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"36"},"PeriodicalIF":4.7,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11164840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141311973","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":"Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.","authors":"Arturo Martinez-Rodrigo, Jose Carlos Castillo, Alicia Saz-Lara, Iris Otero-Luis, Iván Cavero-Redondo","doi":"10.1007/s13755-024-00292-9","DOIUrl":"10.1007/s13755-024-00292-9","url":null,"abstract":"<p><strong>Purpose: </strong>Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health.</p><p><strong>Methods: </strong>This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions.</p><p><strong>Results: </strong>The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention.</p><p><strong>Conclusion: </strong>This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"34"},"PeriodicalIF":4.7,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11068708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140865388","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":"Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset.","authors":"Ayşe Ayyüce Demirbaş, Hüseyin Üzen, Hüseyin Fırat","doi":"10.1007/s13755-024-00290-x","DOIUrl":"10.1007/s13755-024-00290-x","url":null,"abstract":"<p><p>Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn's disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture's results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"32"},"PeriodicalIF":4.7,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872890","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 hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification.","authors":"Hüseyin Üzen, Hüseyin Fırat","doi":"10.1007/s13755-024-00291-w","DOIUrl":"10.1007/s13755-024-00291-w","url":null,"abstract":"<p><p>White blood cells (WBC) play an effective role in the body's defense against parasites, viruses, and bacteria in the human body. Also, WBCs are categorized based on their morphological structures into various subgroups. The number of these WBC types in the blood of non-diseased and diseased people is different. Thus, the study of WBC classification is quite significant for medical diagnosis. Due to the widespread use of deep learning in medical image analysis in recent years, it has also been used in WBC classification. Moreover, the ConvMixer and Swin transformer models, recently introduced, have garnered significant success by attaining efficient long contextual characteristics. Based on this, a new multipath hybrid network is proposed for WBC classification by using ConvMixer and Swin transformer. This proposed model is called Swin Transformer and ConvMixer based Multipath mixer (SC-MP-Mixer). In the SC-MP-Mixer model, firstly, features with strong spatial details are extracted with the ConvMixer. Then Swin transformer effectively handle these features with self-attention mechanism. In addition, the ConvMixer and Swin transformer blocks consist of a multipath structure to obtain better patch representations in the SC-MP-Mixer. To test the performance of the SC-MP-Mixer, experiments were performed on three WBC datasets with 4 (BCCD), 8 (PBC) and 5 (Raabin) classes. The experimental studies resulted in an accuracy of 99.65% for PBC, 98.68% for Raabin, and 95.66% for BCCD. When compared with the studies in the literature and the state-of-the-art models, it was seen that the SC-MP-Mixer had more effective classification results.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"33"},"PeriodicalIF":4.7,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140866538","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 drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules.","authors":"Kadime Göğebakan, Ramazan Ulu, Rahib Abiyev, Melike Şah","doi":"10.1007/s13755-024-00286-7","DOIUrl":"10.1007/s13755-024-00286-7","url":null,"abstract":"<p><p>According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"27"},"PeriodicalIF":4.7,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10960787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140208846","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":"Alterations of DNA methylation profile in peripheral blood of children with simple obesity.","authors":"Yi Ren, Peng Huang, Xiaoyan Huang, Lu Zhang, Lingjuan Liu, Wei Xiang, Liqun Liu, Xiaojie He","doi":"10.1007/s13755-024-00275-w","DOIUrl":"10.1007/s13755-024-00275-w","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the association between DNA methylation and childhood simple obesity.</p><p><strong>Methods: </strong>Genome-wide analysis of DNA methylation was conducted on peripheral blood samples from 41 children with simple obesity and 31 normal controls to identify differentially methylated sites (DMS). Subsequently, gene functional analysis of differentially methylated genes (DMGs) was carried out. After screening the characteristic DMGs based on specific conditions, the methylated levels of these DMS were evaluated and verified by pyrosequencing. Receiver operating characteristic (ROC) curve analysis assessed the predictive efficacy of corresponding DMGs. Finally, Pearson correlation analysis revealed the correlation between specific DMS and clinical data.</p><p><strong>Results: </strong>The overall DNA methylation level in the obesity group was significantly lower than in normal. A total of 241 DMS were identified. Functional pathway analysis revealed that DMGs were primarily involved in lipid metabolism, carbohydrate metabolism, amino acid metabolism, human diseases, among other pathways. The characteristic DMS within the genes Transcription factor A mitochondrial (<i>TFAM</i>) and Piezo type mechanosensitive ion channel component 1(<i>PIEZO1</i>) were recognized as CpG-cg05831083 and CpG-cg14926485, respectively. Furthermore, the methylation level of CpG-cg05831083 significantly correlated with body mass index (BMI) and vitamin D.</p><p><strong>Conclusions: </strong>Abnormal DNA methylation is closely related to childhood simple obesity. The altered methylation of CpG-cg05831083 and CpG-cg14926485 could potentially serve as biomarkers for childhood simple obesity.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-024-00275-w.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"26"},"PeriodicalIF":4.7,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10948706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177068","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":"Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer's disorder detection using EEG signals.","authors":"G Sudha, N Saravanan, M Muthalakshmi, M Birunda","doi":"10.1007/s13755-024-00284-9","DOIUrl":"10.1007/s13755-024-00284-9","url":null,"abstract":"<p><p>Alzheimer's disease is an incurable neurological disorder that damages cognitive abilities, but early identification reduces the symptoms significantly. The absence of competent healthcare professionals has made automatic identification of Alzheimer's disease more crucial since it lessens the amount of work for staff members and improves diagnostic outcomes. The major aim of this work is \"to develop a computer diagnostic scheme that makes it possible to identify AD using the Electroencephalogram (EEG) signal\". Therefore, Dynamically Stabilized Recurrent Neural Network Optimized with Artificial Gorilla Troops espoused Alzheimer's Disorder Detection using EEG signals (DSRNN-AGTO-ADD) is proposed in this paper. Here, Dynamic Context-Sensitive Filter (DCSF) is considered to eliminate the noise, and interference from the EEG signal. Then Adaptive and Concise Empirical Wavelet Transform (ACEWT) is utilized to separate the filtered signals from the frequency bands, and to feature extraction from the EEG signals. Signal's characteristics, like logarithmic bandwidth power, standard deviation, variance, kurtosis, mean energy, mean square, norm are combined to ACEWT method to create feature vectors and enhance diagnostic performance. After that, the extracted features are fed to Dynamically Stabilized Recurrent Neural Network (DSRNN) for task classification. Weight parameter of DSRNN is enhanced using Artificial Gorilla Troops Optimization Algorithm (AGTOA). The proposed DSRNN-AGTOA-ADD algorithm is activated in MATLAB. The metrics including accuracy, specificity, sensitivity, precision, computation time, ROC are examined for AD diagnosis. The performance of the proposed DSRNN-AGTOA-ADD approach attains 12.98%, 5.98% and 23.45% high specificity; 29.98%, 23.32% and 19.76% lower computation Time and 29.29%, 8.365%, 8.551% and 7.915% higher ROC compared with the existing methods.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"25"},"PeriodicalIF":4.7,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10942965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140144280","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":"Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing.","authors":"Sergio Rubio-Martín, María Teresa García-Ordás, Martín Bayón-Gutiérrez, Natalia Prieto-Fernández, José Alberto Benítez-Andrades","doi":"10.1007/s13755-024-00281-y","DOIUrl":"10.1007/s13755-024-00281-y","url":null,"abstract":"<p><strong>Purpose: </strong>The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals.</p><p><strong>Methods: </strong>We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform's API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models.</p><p><strong>Results: </strong>The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD.</p><p><strong>Conclusion: </strong>Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"20"},"PeriodicalIF":4.7,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140060727","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":"Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs.","authors":"Weiguang Wang, Yingying Feng, Haiyan Zhao, Xin Wang, Ruikai Cai, Wei Cai, Xia Zhang","doi":"10.1007/s13755-024-00278-7","DOIUrl":"10.1007/s13755-024-00278-7","url":null,"abstract":"<p><p>Diagnosis 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 electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"15"},"PeriodicalIF":4.7,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10908733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140029263","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}