Journal of Biomedical Informatics最新文献

{"title":"Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping","authors":"Yvon K. Awuklu ,&nbsp;Fleur Mougin ,&nbsp;Romain Griffier ,&nbsp;Meghyn Bienvenu ,&nbsp;Vianney Jouhet","doi":"10.1016/j.jbi.2025.104808","DOIUrl":"10.1016/j.jbi.2025.104808","url":null,"abstract":"<div><h3>Objective:</h3><div>To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers.</div></div><div><h3>Materials and Methods:</h3><div>We crafted an ontology to encapsulate crucial domain knowledge, leveraging it to query the Clinical Data Warehouse (CDW) of Bordeaux University Hospital. Our work aimed at accurately representing domain knowledge and identifying inconsistencies through ontological axioms. Specifically, our aim was to pinpoint lung cancer patients with EGFR or ALK mutations treated with tyrosine kinase inhibitors (TKIs). We evaluated the ability of this ontology to retrieve and characterize patients in comparison with a traditional SQL queries executed on the CDW.</div></div><div><h3>Results:</h3><div>The ontology’s results closely aligned with those of the SQL queries. A sub-cohort of 60 lung cancer patients with conflicting information was identified, highlighting inconsistencies in the data. Moreover, the ontology complemented the existing data, uncovering additional information and enriching the dataset.</div></div><div><h3>Discussion:</h3><div>This work has highlighted challenges in managing temporal data and handling imperfect data. Addressing these challenges is essential for the effective use of CDW in phenotyping.</div></div><div><h3>Conclusion:</h3><div>Ontologies improve data quality by identifying inconsistencies, enhancing data completeness, facilitating complex SQL queries, and standardize processes. Developing a framework to manage inconsistent healthcare data, considering its temporal nature, is essential.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104808"},"PeriodicalIF":4.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A graph neural network explainability strategy driven by key subgraph connectivity","authors":"L.N. Dai ,&nbsp;D.H. Xu ,&nbsp;Y.F. Gao","doi":"10.1016/j.jbi.2025.104813","DOIUrl":"10.1016/j.jbi.2025.104813","url":null,"abstract":"<div><div>Current explainability strategies for Graph Neural Networks (GNNs) often focus on individual nodes or edges, neglecting the significance of key subgraphs in decision-making processes. This limitation can result in dispersed and less reliable explanatory outcomes, particularly for complex tasks. This paper proposes a key subgraph retrieval method based on Euclidean distance, leveraging node representations obtained through training on the BA3 and Mutagenicity datasets to interpret GNN decisions. The proposed method achieves accuracies of 99.25% and 82.40% on the respective datasets. Performance comparison experiments with other mainstream explainability strategies, along with visualization analyses, demonstrate the effectiveness and robustness of this approach.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104813"},"PeriodicalIF":4.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care","authors":"Andrea Campagner ,&nbsp;Luca Marconi ,&nbsp;Edoardo Bianchi ,&nbsp;Beatrice Arosio ,&nbsp;Paolo Rossi ,&nbsp;Giorgio Annoni ,&nbsp;Tiziano Angelo Lucchi ,&nbsp;Nicola Montano ,&nbsp;Federico Cabitza","doi":"10.1016/j.jbi.2025.104799","DOIUrl":"10.1016/j.jbi.2025.104799","url":null,"abstract":"<div><h3>Objective:</h3><div>Dementia represents a growing public health challenge, affecting an increasing number of individuals. It encompasses a broad spectrum of cognitive impairments, ranging from mild to severe stages, each of which demands varying levels of care. Current diagnostic approaches often treat dementia as a uniform condition, potentially overlooking clinically significant subtypes, which limits the effectiveness of treatment and care strategies. This study seeks to address the limitations of traditional diagnostic methods by applying unsupervised machine learning techniques to a large, multi-modal dataset of dementia patients (encompassing multiple data sources including clinical, demographic, gene expression and protein concentrations), with the aim of identifying distinct subtypes within the population. The primary focus is on differentiating between mild and severe stages of dementia to improve diagnostic accuracy and personalize treatment plans.</div></div><div><h3>Methods:</h3><div>The dataset analyzed included 911 individuals, described by 157 multi-modal characteristics, encompassing clinical, genomic, proteomic and sociodemographic features. After handling missing data, the dataset was reduced to 561 rows and 135 columns. Various dimensionality reduction techniques were applied to improve the feature-to-patient ratio, and unsupervised clustering methods were employed to identify potential subtypes. The major novelty in our methodology regards the combination of different techniques, bridging high-dimensional statistical inference, multi-modal dimensionality reduction and clustering analysis, to appropriately model the multi-modal nature of the data and ensure clinical relevance.</div></div><div><h3>Results:</h3><div>The analysis revealed distinct clusters within the dementia population, each characterized by specific clinical and demographic profiles. These profiles included variations in biomarkers, cognitive scores, and disability levels. The findings suggest the presence of previously unrecognized subgroups, distinguished by their genomic, proteomic, and clinical characteristics.</div></div><div><h3>Conclusion:</h3><div>This study demonstrates that unsupervised machine learning can effectively identify clinically relevant subtypes of dementia, with important implications for diagnosis and personalized treatment. Further research is required to validate these findings and investigate their potential to improve patient outcomes.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104799"},"PeriodicalIF":4.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Language-Guided Progressive Fusion Network with semantic density alignment for Medical Visual Question Answering","authors":"Shuxian Du ,&nbsp;Shuang Liang ,&nbsp;Yu Gu","doi":"10.1016/j.jbi.2025.104811","DOIUrl":"10.1016/j.jbi.2025.104811","url":null,"abstract":"<div><div>Medical Visual Question Answering (Med-VQA) is a critical multimodal task with the potential to address the scarcity and imbalance of medical resources. However, most existing studies overlook the limitations of the inconsistency in information density between medical images and text, as well as the long-tail distribution in datasets, which continue to make Med-VQA an open challenge. To overcome these issues, this study proposes a Language-Guided Progressive Fusion Network (LGPFN) with three key modules: Question-Guided Progressive Multimodal Fusion (QPMF), Language-Gate Mechanism (LGM), and Triple Semantic Feature Alignment (TriSFA). QPMF progressively guides the fusion of visual and textual features using both global and local question representations. LGM, a linguistic rule-based module, distinguishes between Closed-Ended (CE) and Open-Ended (OE) samples, directing the fused features to the appropriate classifiers. Finally, TriSFA captures the rich semantic information of OE answers and mine the underlying associations among fused features, predicted answers, and ground truths, aligning them in a ternary semantic feature space. The proposed LGPFN framework outperforms existing state-of-the-art models, achieving the best overall accuracies of 80.39%, 84.07%, 75.74%, and 70.60% on the VQA-RAD, SLAKE, PathVQA, and VQA-Med 2019 datasets, respectively. These results demonstrate the effectiveness and generalizability of the proposed model, underscoring its potential as a medical Artificial Intelligent (AI) agent that could benefit universal health coverage.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104811"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tentative renderings: Describing local data infrastructures that support the implementation and evaluation of national evaluation Initiatives","authors":"Jennifer Van Tiem ,&nbsp;Nicole L. Johnson ,&nbsp;Erin Balkenende ,&nbsp;DeShauna Jones ,&nbsp;Julia E. Friberg Walhof ,&nbsp;Emily E. Chasco ,&nbsp;Jane Moeckli ,&nbsp;Kenda S. Steffensmeier ,&nbsp;Melissa J.A. Steffen ,&nbsp;Kanika Arora ,&nbsp;Borsika A. Rabin ,&nbsp;Heather Schacht Reisinger","doi":"10.1016/j.jbi.2025.104814","DOIUrl":"10.1016/j.jbi.2025.104814","url":null,"abstract":"<div><h3>Objective</h3><div>Data journeys are a way to describe and interrogate “the life of data” (Bates et al 2010). Thus far, they have been used to clarify the mobile nature of data by visualizing the pathways made by handling and moving data. We wanted to use the data journeys method (Eleftheriou et al. 2018) to compare different data journeys by noticing repetitions, patterns, and gaps.</div></div><div><h3>Methods</h3><div>We conducted qualitative interviews with 43 evaluators, implementers and administrators associated with 21 clinical and training programs, called “Enterprise-Wide Initiatives” (EWIs) that are part of a national health system in the United States. We used inductive and deductive coding to identify narratives of data journeys, and then we used the “swim lane” (Collar et al 2012) format to make data journey maps based on those narratives.</div></div><div><h3>Results</h3><div>Unlike the actors in Eleftheriou et al. (2018)’s work, who built a data infrastructure to manage clinical data, the actors in our study built data infrastructures to evaluate clinical data. We created and compared two data journey maps that helped us explore differences in data production and management. In tracing the pathways available to the data entity of interest, and the processes through which the actors interacted with it, we noticed how the same piece of information was made to work in different ways.</div></div><div><h3>Conclusions</h3><div>Researchers often must build a new data infrastructures to respond to the unique needs of their evaluation work. Differing abilities lead to differences in what programs can build, and consequently what kinds of evaluation work they can support. With the goal of straightforward comparisons across different programs, a more limited focus on quantitative values, and a better description of the data journeys used by the evaluation teams, might facilitate more nuanced assessments of the evidence of complex outcomes.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104814"},"PeriodicalIF":4.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning","authors":"Emanuele Tauro ,&nbsp;Alessandra Gorini ,&nbsp;Grzegorz Bilo ,&nbsp;Enrico Gianluca Caiani","doi":"10.1016/j.jbi.2025.104815","DOIUrl":"10.1016/j.jbi.2025.104815","url":null,"abstract":"<div><h3>Objective</h3><div>Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.</div></div><div><h3>Methods</h3><div>A novel approach based on self-supervised machine learning (SSML) was proposed. A training-test split was performed (80 % - 20 %), with the training set used for Personas development. The obtained labels were used as input for a 5-fold cross-validation grid search, resulting in 5 optimal different models. The “weak” ground truth for the test set was determined using the trained clustering model, and was compared with the prediction obtained by the majority voting of the optimal models. Performance evaluation was conducted by means of weighted accuracy, precision, recall and F1 score.</div></div><div><h3>Results</h3><div>The proposed method was evaluated on two very different healthcare datasets composed by questionnaires. The former was presented 1070 subjects, resulting in three unbalanced Personas (P0 n = 100; P1 n = 292; P2 n = 464). The latter included 176 subjects with three slightly unbalanced Personas. (P0 n = 58; P1 n = 32; P2 n = 50). The SSML approach resulted capable of correctly differentiating the clusters with high values of weighted accuracy (88.27 % and 94.12 %), precision (87.11 % and 92.83 %), recall (86.92 % and 91.67 %), and F1 score (86.92 % and 91.76 %).</div></div><div><h3>Conclusions</h3><div>The proposed method showed high capabilities in generalization beyond the training data, validating the Personas’ capability of stratifying the characteristics of target populations. Additionally, this method significantly reduced the costs to validate Personas when compared to other methods in current literature.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104815"},"PeriodicalIF":4.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedicalGLM: A Pediatric Medical Question Answering Model with a quality evaluation mechanism","authors":"Xin Wang ,&nbsp;Zhaocai Sun ,&nbsp;Pingping Wang ,&nbsp;Benzheng Wei","doi":"10.1016/j.jbi.2025.104793","DOIUrl":"10.1016/j.jbi.2025.104793","url":null,"abstract":"<div><h3>Objective:</h3><div>Large Language models (LLMs) have a wide range of medical applications, especially in scenarios such as question-answering. However, existing models face the challenge of accurately assessing the quality of information when generating medical information, which may lead to the inability to effectively distinguish beneficial and harmful information, thus affecting the quality of question-answering. This study aims to improve the information quality and practicability of medical question-answering.</div></div><div><h3>Methods:</h3><div>This study proposes MedicalGLM, a fine-tuning model based on a quality evaluation mechanism. Specifically, MedicalGLM contains a reward model for assessing the quality of medical QA. It adjusts its training process by returning the assessment scores to the QA model as penalties through a quality score loss function.</div></div><div><h3>Results:</h3><div>The experimental results indicate that MedicalGLM achieved the highest scores among the evaluated models in the Rouge-1, Rouge-2, Rouge-L, and BLEU metrics, with values of 54.90, 28.02, 44.50, and 32.61, respectively. Its proficiency in generating responses for the pediatric medical quiz task is notably superior to other prevailing LLMs in the medical domain.</div></div><div><h3>Conclusion:</h3><div>MedicalGLM significantly improves the quality and practicability of the generated information of the medical question-answering model by introducing a quality evaluation mechanism, which provides an effective improvement idea for researching medical large language models. Our code and model are publicly available for further research on <span><span>https://github.com/wangxinwwang/MedicalGLM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104793"},"PeriodicalIF":4.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143585855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FedIMPUTE: Privacy-preserving missing value imputation for multi-site heterogeneous electronic health records","authors":"Siqi Li ,&nbsp;Mengying Yan ,&nbsp;Ruizhi Yuan ,&nbsp;Molei Liu ,&nbsp;Nan Liu ,&nbsp;Chuan Hong","doi":"10.1016/j.jbi.2025.104780","DOIUrl":"10.1016/j.jbi.2025.104780","url":null,"abstract":"<div><h3>Objectives:</h3><div>We propose FedIMPUTE, a communication-efficient federated learning (FL) based approach for missing value imputation (MVI). Our method enables multiple sites to collaboratively perform MVI in a privacy-preserving manner, addressing challenges of data-sharing constraints and population heterogeneity.</div></div><div><h3>Methods:</h3><div>We begin by conducting MVI locally at each participating site, followed by the application of various FL strategies, ranging from basic to advanced, to federate local MVI models without sharing site-specific data. The federated model is then broadcast and used by each site for MVI. We evaluate FedIMPUTE using both simulation studies and a real-world application on electronic health records (EHRs) to predict emergency department (ED) outcomes as a proof of concept.</div></div><div><h3>Results:</h3><div>Simulation studies show that FedIMPUTE outperforms all baseline MVI methods under comparison, improving downstream prediction performance and effectively handling data heterogeneity across sites. By using ED datasets from three hospitals within the Duke University Health System (DUHS), FedIMPUTE achieves the lowest mean squared error (MSE) among benchmark MVI methods, indicating superior imputation accuracy. Additionally, FedIMPUTE provides good downstream prediction performance, outperforming or matching other benchmark methods.</div></div><div><h3>Conclusion:</h3><div>FedIMPUTE enhances the performance of downstream risk prediction tasks, particularly for sites with high missing data rates and small sample sizes. It is easy to implement and communication-efficient, requiring sites to share only non-patient-level summary statistics.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104780"},"PeriodicalIF":4.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143585854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing generalization of medical image segmentation via game theory-based domain selection","authors":"Zuyu Zhang ,&nbsp;Yan Li ,&nbsp;Byeong-Seok Shin","doi":"10.1016/j.jbi.2025.104802","DOIUrl":"10.1016/j.jbi.2025.104802","url":null,"abstract":"<div><div>Medical image segmentation models often fail to generalize well to new datasets due to substantial variability in imaging conditions, anatomical differences, and patient demographics. Conventional domain generalization (DG) methods focus on learning domain-agnostic features but often overlook the importance of maintaining performance balance across different domains, leading to suboptimal results. To address these issues, we propose a novel approach using game theory to model the training process as a zero-sum game, aiming for a Nash equilibrium to enhance adaptability and robustness against domain shifts. Specifically, our adaptive domain selection method, guided by the Beta distribution and optimized via reinforcement learning, dynamically adjusts to the variability across different domains, thus improving model generalization. We conducted extensive experiments on benchmark datasets for polyp segmentation, optic cup/optic disc (OC/OD) segmentation, and prostate segmentation. Our method achieved an average Dice score improvement of 1.75% compared with other methods, demonstrating the effectiveness of our approach in enhancing the generalization performance of medical image segmentation models.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"164 ","pages":"Article 104802"},"PeriodicalIF":4.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contextual information contributes to biomedical named entity normalization","authors":"Gengxin Luo ,&nbsp;Nannan Shi ,&nbsp;Gang Wang ,&nbsp;Buzhou Tang","doi":"10.1016/j.jbi.2025.104806","DOIUrl":"10.1016/j.jbi.2025.104806","url":null,"abstract":"<div><h3>Objective:</h3><div>As one of the most crucial upstream tasks in biomedical informatics, biomedical named entity normalization (BNEN) aims to map mentioned named entities to uniform standard identifiers or terms. Most existing methods only consider the similarity between the individual mention itself and its candidates, however, ignore the valuable information of the context around the mention, which is also very important to understand the real semantic of the mention when it is ambiguous.</div></div><div><h3>Material and Methods:</h3><div>In this paper, based on IA-BIOSYN, a representative SOTA (state-of-the-art) BNEN method, we propose a novel BNEN method with contextual information fusion, called CIFSYN, where the context of a given mention is comprehensively considered by putting the given mention’s candidates in the same context of the mention, and the contextual information fusion module is introduced to capture the relationship among the mention, candidates, and context.</div></div><div><h3>Results:</h3><div>Experiments on five public BNEN datasets show that our proposed method achieves Acc@1 of 0.934, 0.937, 0.969, 0.959, and 0.856 on NCBI-Disease, BC5CDR-Disease, BC5CDR-Chemical, TAC2017-ADR, and COMETA, respectively, significantly better than other existing SOTA methods, and the introduced context information module brings a 0.5% improvement in Acc@1 on average.</div></div><div><h3>Conclusion:</h3><div>Contextual information around the mention improves the performance of biomedical named entity normalization.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104806"},"PeriodicalIF":4.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143567235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}