Journal of Biomedical Informatics最新文献

{"title":"A practical guide to usability questionnaires that evaluate clinicians’ perceptions of health information technology","authors":"Alissa L. Russ-Jara ,&nbsp;Jason J. Saleem ,&nbsp;Jennifer Herout","doi":"10.1016/j.jbi.2025.104822","DOIUrl":"10.1016/j.jbi.2025.104822","url":null,"abstract":"<div><h3>Objective</h3><div>Numerous usability questionnaires are available to evaluate the usability of health information technology (IT). It can be difficult for practitioners to determine which questionnaire most closely aligns with their health IT evaluation goals. Our objective was to develop a practical guide to enable practitioners to select an appropriate usability questionnaire for their health IT evaluation.</div></div><div><h3>Methods</h3><div>Questionnaires were identified from the literature and input from usability experts. Inclusion criteria included: 1) post-test or post-task usability questionnaire; 2) demonstrated validity, with good internal reliability (Cronbach α ≥ 0.70); 3) freely available for use; 4) applicable across a wide range of health IT products; and 5) demonstrated use with health IT in peer-reviewed literature, even if not originally designed for healthcare.</div></div><div><h3>Results</h3><div>Criteria were met by seven usability questionnaires. Results include a synopsis of each usability questionnaire along with a matrix to visually compare methodological characteristics across questionnaires. Additionally, results include an analysis of distinguishing methodological strengths and limitations that set each usability questionnaire apart. For each questionnaire, we also outline considerations for use when evaluating health IT.</div></div><div><h3>Conclusion</h3><div>This novel, practical guide provides an important methodological analysis of currently available usability questionnaires for health IT evaluation. This article can help practitioners make a more efficient, but also well-informed, choice when selecting a usability questionnaire for health IT evaluation. This practical, methodological guide applies to a wide range of health IT products, including electronic health records (EHRs).</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104822"},"PeriodicalIF":4.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772547","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":"Scalable and efficient on-chain data management in blockchain for large biomedical data","authors":"Eric Ni ,&nbsp;Elizabeth Knight ,&nbsp;Mark Gerstein","doi":"10.1016/j.jbi.2025.104818","DOIUrl":"10.1016/j.jbi.2025.104818","url":null,"abstract":"<div><div>Blockchain technology is gaining traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in health data management. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain’s strong immutability by storing data directly on-chain. In this work, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, highlighting the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, by designing and implementing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves 2x faster data insertion, 500x faster retrieval, 60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104818"},"PeriodicalIF":4.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753041","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 systematic mapping study of semantic technologies in multi-omics data integration","authors":"Giovanni Maria De Filippis,&nbsp;Domenico Amalfitano,&nbsp;Cristiano Russo,&nbsp;Cristian Tommasino,&nbsp;Antonio Maria Rinaldi","doi":"10.1016/j.jbi.2025.104809","DOIUrl":"10.1016/j.jbi.2025.104809","url":null,"abstract":"<div><h3>Objective:</h3><div>The integration of multi-omics data is essential for understanding complex biological systems, providing insights beyond single-omics approaches. However, challenges related to data heterogeneity, standardization, and computational scalability persist. This study explores the interdisciplinary application of semantic technologies to enhance data integration, standardization, and analysis in multi-omics research.</div></div><div><h3>Methods:</h3><div>We performed a systematic mapping study assessing literature from 2014 to 2024, focusing on the utilization of ontologies, knowledge graphs, and graph-based methods for multi-omics integration.</div></div><div><h3>Results:</h3><div>Our findings indicate a growing number of publications in this field, predominantly appearing in high-impact journals. The deployment of semantic technologies has notably improved data visualization, querying, and management, thus enhancing gene and pathway discovery, and providing deeper disease insights and more accurate predictive modeling.</div></div><div><h3>Conclusion:</h3><div>The study underscores the significance of semantic technologies in overcoming multi-omics integration challenges. Future research should focus on integrating diverse data types, developing advanced computational tools, and incorporating AI and machine learning to foster personalized medicine applications.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104809"},"PeriodicalIF":4.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738327","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":"PLAGCA: Predicting protein–ligand binding affinity with the graph cross-attention mechanism","authors":"Ming-Hui Shi ,&nbsp;Shao-Wu Zhang ,&nbsp;Qing-Qing Zhang ,&nbsp;Yong Han ,&nbsp;Shanwen Zhang","doi":"10.1016/j.jbi.2025.104816","DOIUrl":"10.1016/j.jbi.2025.104816","url":null,"abstract":"<div><div>Accurate prediction of protein–ligand binding affinity plays a crucial role in drug discovery. However, determining the binding affinity of protein–ligands through biological experimental approaches is both time-consuming and expensive. Although some computational methods have been developed to predict protein–ligands binding affinity, most existing methods extract the global features of proteins and ligands through separate encoders, without considering to extract the local pocket interaction features of protein–ligand complexes, resulting in the limited prediction accuracy. In this work, we proposed a novel Protein–Ligand binding Affinity prediction method (named PLAGCA) by introducing Graph Cross-Attention mechanism to learn the local three-dimensional (3D) features of protein–ligand pockets, and integrating the global sequence/string features and local graph interaction features of protein–ligand complexes. PLAGCA uses sequence encoding and self-attention to extract the protein/ligand global features from protein FASTA sequences/ligand SMILES strings, adopts graph neural network and cross-attention to extract the protein–ligand local interaction features from the molecular structures of protein binding pockets and ligands. All these features are concatenated and input into a multi-layer perceptron (MLP) for predicting the protein–ligand binding affinity. The experimental results show that our PLAGCA outperforms other state-of-the-art computational methods, and it can effectively predict protein–ligand binding affinity with superior generalization capability. PLAGCA can capture the critical functional residues that are important contribution to the protein–ligand binding.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104816"},"PeriodicalIF":4.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725274","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":"Integrating Mendelian randomization and literature-mined evidence for breast cancer risk factors","authors":"Marina Vabistsevits ,&nbsp;Timothy Robinson ,&nbsp;Ben Elsworth ,&nbsp;Yi Liu ,&nbsp;Tom R. Gaunt","doi":"10.1016/j.jbi.2025.104810","DOIUrl":"10.1016/j.jbi.2025.104810","url":null,"abstract":"<div><h3>Objective:</h3><div>An increasing challenge in population health research is efficiently utilising the wealth of data available from multiple sources to investigate disease mechanisms and identify potential intervention targets. The use of biomedical data integration platforms can facilitate evidence triangulation from these different sources, improving confidence in causal relationships of interest. In this work, we aimed to integrate Mendelian randomization (MR) and literature-mined evidence from the EpiGraphDB biomedical knowledge graph to build a comprehensive overview of risk factors for developing breast cancer.</div></div><div><h3>Methods:</h3><div>We utilised MR-EvE (“Everything-vs-Everything”) data to identify candidate risk factors for breast cancer and generate hypotheses for potential mediators of their effect. We also integrated this data with literature-mined relationships, which were extracted by overlapping literature spaces of risk factors and breast cancer. The literature-based discovery (LBD) results were followed up by validation with two-step MR to triangulate the findings from two data sources.</div></div><div><h3>Results:</h3><div>We identified 129 novel and established lifestyle risk factors and molecular traits with evidence of an effect on breast cancer, and made the MR results available in an R/Shiny app (<span><span>https://mvab.shinyapps.io/MR_heatmaps/</span><svg><path></path></svg></span>). We developed an LBD approach for identifying potential mechanistic intermediates of identified risk factors. We present the results of MR and literature evidence integration for two case studies (childhood body size and HDL-cholesterol), demonstrating their complementary functionalities.</div></div><div><h3>Conclusion:</h3><div>We demonstrate that MR-EvE data offers an efficient hypothesis-generating approach for identifying disease risk factors. Moreover, we show that integrating MR evidence with literature-mined data may be used to identify causal intermediates and uncover the mechanisms behind the disease.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104810"},"PeriodicalIF":4.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700461","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":"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}