{"title":"Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals.","authors":"Neha Prerna Tigga, Shruti Garg","doi":"10.1007/s13755-022-00205-8","DOIUrl":"10.1007/s13755-022-00205-8","url":null,"abstract":"<p><strong>Purpose: </strong>Depression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG) signals represent the functional state of the human brain and can help build an accurate and viable technique for the early prediction and treatment of depression.</p><p><strong>Methods: </strong>An attention-based gated recurrent units transformer (AttGRUT) time-series model is proposed to efficiently identify EEG perturbations in depressive patients. Statistical, spectral and wavelet features were first extracted from the 60-channel EEG signal data. Then, two feature selection techniques, recursive feature elimination and the Boruta algorithm, both with Shapley additive explanations, were utilised for selecting essential features.</p><p><strong>Results: </strong>The proposed model outperformed the two baseline and two hybrid time-series models-long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network-LSTM (CNN-LSTM), and CNN-GRU-achieving an accuracy of up to 98.67%. Feature selection considerably increased the performance across all time-series models.</p><p><strong>Conclusion: </strong>Based on the obtained results, novel feature selection greatly affected the results of the baseline and hybrid time-series models. The proposed AttGRUT can be implemented and tested in other domains by using different modalities for prediction.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-022-00205-8.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"1"},"PeriodicalIF":4.7,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10276794","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}
Sara Lombardi, Petri Partanen, Piergiorgio Francia, Italo Calamai, Rossella Deodati, Marco Luchini, Rosario Spina, Leonardo Bocchi
{"title":"Classifying sepsis from photoplethysmography.","authors":"Sara Lombardi, Petri Partanen, Piergiorgio Francia, Italo Calamai, Rossella Deodati, Marco Luchini, Rosario Spina, Leonardo Bocchi","doi":"10.1007/s13755-022-00199-3","DOIUrl":"https://doi.org/10.1007/s13755-022-00199-3","url":null,"abstract":"<p><p>Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated immune response to an infection and is one of the leading causes of death in the intensive care unit (ICU). Early detection and treatment of sepsis can increase the survival rate of patients. The use of devices such as the photoplethysmograph could allow the early evaluation in addition to continuous monitoring of septic patients. The aim of this study was to verify the possibility of detecting sepsis in patients from whom the photoplethysmographic signal was acquired via a pulse oximeter. In this work, we developed a deep learning-based model for sepsis identification. The model takes a single input, the photoplethysmographic signal acquired by pulse oximeter, and performs a binary classification between septic and nonseptic samples. To develop the method, we used MIMIC-III database, which contains data from ICU patients. Specifically, the selected dataset includes 85 septic subjects and 101 control subjects. The PPG signals acquired from these patients were segmented, processed and used as input for the developed model with the aim of identifying sepsis. The proposed method achieved an accuracy of 76.37% with a sensitivity of 70.95% and a specificity of 81.04% on the test set. As regards the ROC curve, the Area Under Curve reached a value of 0.842. The results of this study indicate how the plethysmographic signal can be used as a warning sign for the early detection of sepsis with the aim of reducing the time for diagnosis and therapeutic intervention. Furthermore, the proposed method is suitable for integration in continuous patient monitoring.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":" ","pages":"30"},"PeriodicalIF":6.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40443779","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}
Nazmus Sakib, Kathryn Hyer, Debra Dobbs, Lindsay Peterson, Dylan J Jester, Nan Kong, Mingyang Li
{"title":"A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response.","authors":"Nazmus Sakib, Kathryn Hyer, Debra Dobbs, Lindsay Peterson, Dylan J Jester, Nan Kong, Mingyang Li","doi":"10.1007/s13755-022-00190-y","DOIUrl":"10.1007/s13755-022-00190-y","url":null,"abstract":"<p><p>Nursing homes (NHs) are responsible for caring for frail, older adults, who are highly vulnerable to natural disasters, such as hurricanes. Due to the influence of highly uncertain environmental conditions and varied NH characteristics (e.g., geo-location, staffing, residents' health conditions), the NH evacuation response, namely evacuating or sheltering-in-place, is highly uncertain. Accurate prediction of NH evacuation response is important for emergency management agencies to accurately anticipate the NH evacuation demand surge with healthcare resources proactively planned. Existing hurricane evacuation research mainly focuses on the general population. For NH evacuation, existing studies mainly focus on conceptual studies and/or qualitative analysis using a single source of data, such as surveys or resident health data. There is a lack of research to develop analytics-based method by fusing rich environmental data with NH data to improve the prediction accuracy. In this paper, we propose a Geographic Information System (GIS) data enhanced predictive analytics approach for forecasting NH evacuation response by fusing multi-source data related to storm conditions, geographical information, NH organizational characteristics as well as staffing and residents characteristics of each NH. In particular, multiple GIS features, such as distance to storm trajectory, projected wind speed, potential storm surge and NH elevation, are extracted from rich GIS information and incorporated to improve the prediction performance. A real-world case study of NH evacuation during Hurricane Irma in 2017 is examined to demonstrate superior prediction performance of the proposed work over a large number of predictive analytics methods without GIS information.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"10 1","pages":"28"},"PeriodicalIF":6.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9474783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10106276","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}
Yong Zhang, Ming Sheng, Xingyue Liu, Ruoyu Wang, Weihang Lin, Peng Ren, Xia Wang, Enlai Zhao, Wenchao Song
{"title":"A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration.","authors":"Yong Zhang, Ming Sheng, Xingyue Liu, Ruoyu Wang, Weihang Lin, Peng Ren, Xia Wang, Enlai Zhao, Wenchao Song","doi":"10.1007/s13755-022-00183-x","DOIUrl":"https://doi.org/10.1007/s13755-022-00183-x","url":null,"abstract":"<p><p>Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases-data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":" ","pages":"22"},"PeriodicalIF":6.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9417071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33446510","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}
Chaohui Guo, Shaofu Lin, Zhisheng Huang, Yahong Yao
{"title":"Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM.","authors":"Chaohui Guo, Shaofu Lin, Zhisheng Huang, Yahong Yao","doi":"10.1007/s13755-022-00184-w","DOIUrl":"https://doi.org/10.1007/s13755-022-00184-w","url":null,"abstract":"<p><p>With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the \"Tree Hole\". The purpose of this article is to support the \"Tree Hole\" rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of \"Tree Hole\" named \"Zou Fan\", this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of \"Tree Hole\" messages in multiple time dimensions is positively correlated to emotion. The longer the \"Tree Hole\" is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of \"Tree Hole\" rescue, volunteers should focus on the long-formed \"Tree Hole\" and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":" ","pages":"15"},"PeriodicalIF":6.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40602250","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":"Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression.","authors":"Qingfeng Tang, Shoujiang Xu, Mengjuan Guo, Guangjun Wang, Zhigeng Pan, Benyue Su","doi":"10.1007/s13755-022-00172-0","DOIUrl":"https://doi.org/10.1007/s13755-022-00172-0","url":null,"abstract":"<p><strong>Purpose: </strong>Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal.</p><p><strong>Methods: </strong>Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices.</p><p><strong>Results: </strong>Compared with the CA, the VA is closer to PW (<i>r</i> = 0.539, <i>P</i> < 0.001 to <i>r</i> = 0.589, <i>P</i> < 0.001 in men; <i>r</i> = 0.325, <i>P</i> < 0.001 to <i>r</i> = 0.400, <i>P</i> < 0.001 in women), IPA (<i>r</i> = - 0.446, <i>P</i> < 0.001 to <i>r</i> = - 0.534, <i>P</i> < 0.001 in men; <i>r</i> = - 0.623, <i>P</i> < 0.001 to <i>r</i> = - 0.660, <i>P</i> < 0.001 in women), RBA (<i>r</i> = 0.328, <i>P</i> < 0.001 to <i>r</i> = 0.371, <i>P</i> < 0.001 in women), AIx (<i>r</i> = 0.659, <i>P</i> < 0.001 to <i>r</i> = 0.738, <i>P</i> < 0.001 in men; <i>r</i> = 0.547, <i>P</i> < 0.001 to <i>r</i> = 0.573, <i>P</i> < 0.001 in women), DAI (<i>r</i> = 0.517, <i>P</i> < 0.001 to <i>r</i> = 0.532, <i>P</i> < 0.001 in men; <i>r</i> = 0.507, <i>P</i> < 0.001 to <i>r</i> = 0.570, <i>P</i> < 0.001 in women) and PTT (<i>r</i> = 0.526, <i>P</i> < 0.001 to <i>r</i> = 0.659, <i>P</i> < 0.001 in men; <i>r</i> = 0.577, <i>P</i> < 0.001 to <i>r</i> = 0.814, <i>P</i> < 0.001 in women).</p><p><strong>Conclusion: </strong>The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"10 1","pages":"7"},"PeriodicalIF":6.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138471037","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}
Petros Barmpas, Sotiris Tasoulis, Aristidis G Vrahatis, Spiros V Georgakopoulos, Panagiotis Anagnostou, Matthew Prina, José Luis Ayuso-Mateos, Jerome Bickenbach, Ivet Bayes, Martin Bobak, Francisco Félix Caballero, Somnath Chatterji, Laia Egea-Cortés, Esther García-Esquinas, Matilde Leonardi, Seppo Koskinen, Ilona Koupil, Andrzej Paja K, Martin Prince, Warren Sanderson, Sergei Scherbov, Abdonas Tamosiunas, Aleksander Galas, Josep Maria Haro, Albert Sanchez-Niubo, Vassilis P Plagianakos, Demosthenes Panagiotakos
{"title":"A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project.","authors":"Petros Barmpas, Sotiris Tasoulis, Aristidis G Vrahatis, Spiros V Georgakopoulos, Panagiotis Anagnostou, Matthew Prina, José Luis Ayuso-Mateos, Jerome Bickenbach, Ivet Bayes, Martin Bobak, Francisco Félix Caballero, Somnath Chatterji, Laia Egea-Cortés, Esther García-Esquinas, Matilde Leonardi, Seppo Koskinen, Ilona Koupil, Andrzej Paja K, Martin Prince, Warren Sanderson, Sergei Scherbov, Abdonas Tamosiunas, Aleksander Galas, Josep Maria Haro, Albert Sanchez-Niubo, Vassilis P Plagianakos, Demosthenes Panagiotakos","doi":"10.1007/s13755-022-00171-1","DOIUrl":"10.1007/s13755-022-00171-1","url":null,"abstract":"<p><p>The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP).</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-022-00171-1.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"10 1","pages":"6"},"PeriodicalIF":4.7,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10866298","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":"CD-Surv: a contrastive-based model for dynamic survival analysis.","authors":"Caogen Hong, Jinbiao Chen, Fan Yi, Yuzhe Hao, Fanwen Meng, Zhanghuiya Dong, Hui Lin, Zhengxing Huang","doi":"10.1007/s13755-022-00173-z","DOIUrl":"https://doi.org/10.1007/s13755-022-00173-z","url":null,"abstract":"<p><p>Survival analysis, aimed at investigating the relationships between covariates and event time, has exhibited profound effects on health service management. Longitudinal data with sequential patterns, such as electronic health records (EHRs), contain a large volume of patient treatment trajectories, and therefore, provide great potential for survival analysis. However, most existing studies address the survival analysis problem in a static manner, that is, they only utilize a fraction of longitudinal data, ignore the correlations between multiple visits, and usually may not be able to capture the latent representations of patient treatment trajectories. This inevitably deteriorates the performance of the survival analysis. To address this challenge, we propose an end-to-end contrastive-based model <i>CD-Surv</i> to better understand the patient treatment trajectories and dynamically predict the survival probability of a target patient. Specifically, two data augmentation strategies, namely, <i>mask generation</i> and <i>shuffle generation</i>, are adopted to augment the real treatment trajectories documented in the EHR. Based on this, the hidden representations of the real trajectories can be improved by utilizing contrastive learning between augmented and real trajectories. We evaluated our proposed CD-Surv on two real-world datasets, and the experimental results indicated that our proposed model could outperform state-of-the-art baselines on various evaluation metrics.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"10 1","pages":"5"},"PeriodicalIF":6.0,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138470973","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}
Koushik Chandra Howlader, Md Shahriare Satu, Md Abdul Awal, Md Rabiul Islam, Sheikh Mohammed Shariful Islam, Julian M W Quinn, Mohammad Ali Moni
{"title":"Machine learning models for classification and identification of significant attributes to detect type 2 diabetes.","authors":"Koushik Chandra Howlader, Md Shahriare Satu, Md Abdul Awal, Md Rabiul Islam, Sheikh Mohammed Shariful Islam, Julian M W Quinn, Mohammad Ali Moni","doi":"10.1007/s13755-021-00168-2","DOIUrl":"https://doi.org/10.1007/s13755-021-00168-2","url":null,"abstract":"<p><p>Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-021-00168-2.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":" ","pages":"2"},"PeriodicalIF":6.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8828812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39795319","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":"Health Information Science: 11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings","authors":"","doi":"10.1007/978-3-031-20627-6","DOIUrl":"https://doi.org/10.1007/978-3-031-20627-6","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"45 8","pages":""},"PeriodicalIF":6.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72634976","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}