AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第5页

Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models. 基于多标签分类和预训练语言模型的安全网精神病院临床记录自杀表型分析。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Zehan Li, Yan Hu, Scott Lane, Salih Selek, Lokesh Shahani, Rodrigo Machado-Vieira, Jair Soares, Hua Xu, Hongfang Liu, Ming Huang

{"title":"Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models.","authors":"Zehan Li, Yan Hu, Scott Lane, Salih Selek, Lokesh Shahani, Rodrigo Machado-Vieira, Jair Soares, Hua Xu, Hongfang Liu, Ming Huang","doi":"","DOIUrl":"","url":null,"abstract":"Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language models offer promise for identifying suicidality from unstructured clinical narratives. We evaluated the performance of four BERT-based models using two fine-tuning strategies (multiple single-label and single multi-label) for detecting coexisting suicidal events from 500 annotated psychiatric evaluation notes. The notes were labeled for suicidal ideation (SI), suicide attempts (SA), exposure to suicide (ES), and non-suicidal self-injury (NSSI). RoBERTa outperformed other models using binary relevance (acc=0.86, F1=0.78). MentalBERT (F1=0.74) also exceeded BioClinicalBERT (F1=0.72). RoBERTa fine-tuned with a single multi-label classifier further improved performance (acc=0.88, F1=0.81), highlighting that models pre-trained on domain-relevant data and the single multi-label classification strategy enhance efficiency and performance.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"260-269"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building Trust in Clinical AI: A Web-Based Explainable Decision Support System for Chronic Kidney Disease. 在临床人工智能中建立信任：一个基于网络的可解释的慢性肾脏疾病决策支持系统。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Krishna Mridha, Ming Wang, Lijun Zhang

{"title":"Building Trust in Clinical AI: A Web-Based Explainable Decision Support System for Chronic Kidney Disease.","authors":"Krishna Mridha, Ming Wang, Lijun Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Chronic Kidney Disease (CKD) is a significant global public health issue, affecting over 10% of the population. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. We developed a Web-Based Clinical Decision Support System (CDSS) for CKD, incorporating advanced Explainable AI (XAI) methods, specifically SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). The model employs and evaluates multiple classifiers: KNN, Random Forest, AdaBoost, XGBoost, CatBoost, and Extra Trees, to predict CKD. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and the AUC. AdaBoost achieved a 100% accuracy rate. Except for KNN, all classifiers consistently reached perfect precision and sensitivity. Additionally, we present a real-time web-based application to operationalize the model, enhancing trust and accessibility for healthcare practitioners and stakeholder.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"375-384"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating the Impact of Social Determinants of Health on Diagnostic Delays and Access to Antifibrotic Treatment in Idiopathic Pulmonary Fibrosis. 调查健康的社会决定因素对特发性肺纤维化诊断延迟和获得抗纤维化治疗的影响。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Rui Li, Qiuhao Lu, Andrew Wen, Jinlian Wang, Sunyang Fu, Xiaoyang Ruan, Liwei Wang, Hongfang Liu

{"title":"Investigating the Impact of Social Determinants of Health on Diagnostic Delays and Access to Antifibrotic Treatment in Idiopathic Pulmonary Fibrosis.","authors":"Rui Li, Qiuhao Lu, Andrew Wen, Jinlian Wang, Sunyang Fu, Xiaoyang Ruan, Liwei Wang, Hongfang Liu","doi":"","DOIUrl":"","url":null,"abstract":"Idiopathic pulmonary fibrosis (IPF) is a rare disease that is challenging to diagnose. Patients with IPF often spend years awaiting a diagnosis after the onset of initial respiratory symptoms, and only a small percentage receive antifibrotic treatment. In this study, we examine the associations between social determinants of health (SDoH) and two critical factors: time to IPF diagnosis following the onset of initial respiratory symptoms, and whether the patient receives antifibrotic treatment. To approximate individual SDoH characteristics, we extract demographic-specific averages from zip code-level data using the American Community Survey (via the U.S. Census Bureau API). Two classification models are constructed, including logistic regression and XGBoost classification. The results indicate that for time-to-diagnosis, the top three SDoH factors are education, gender, and insurance coverage. Patients with higher education levels and better insurance are more likely to receive a quicker diagnosis, with males having an advantage over females. For antifibrotic treatment, the top three SDoH factors are insurance, gender, and race. Patients with better insurance coverage are more likely to receive antifibrotic treatment, with males and White patients having an advantage over females and patients of other ethnicities. This research may help address disparities in the diagnosis and treatment of IPF related to socioeconomic status.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"280-289"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging GPT-4o for Automated Extraction of Neural Projections from Scientific Literature. 利用gpt - 40从科学文献中自动提取神经投影。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Rashmie Abeysinghe, Gorbachev Jowah, Licong Cui, Samden D Lhatoo, Guo-Qiang Zhang

{"title":"Leveraging GPT-4o for Automated Extraction of Neural Projections from Scientific Literature.","authors":"Rashmie Abeysinghe, Gorbachev Jowah, Licong Cui, Samden D Lhatoo, Guo-Qiang Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Sudden Unexpected Death in Epilepsy (SUDEP) is a major cause of death for epilepsy patients having uncontrolled seizures. Understanding the complex neural circuits within the central nervous system is crucial for understanding the mechanisms underlying cardiorespiratory regulation, particularly in the context of SUDEP. This study explores the potential of GPT-4o, a cutting-edge language model, to automate the extraction of neural projections from scientific literature. We developed prompts to extract neuroscientific structures, extract projections, and perform synonym harmonization. Applying the approach to four neuroscientific articles, the method extracted 205 projections. A random sample of 100 projections identified was handed over to a domain expert for review where 95 were found to be correct. Therefore, GPT-4o was determined to be accurate in parsing complex scientific texts in extracting neural projections. Future work will involve extracting additional entities like techniques and species information for the projections identified.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"32-41"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Positionally Encoded Transformer for Monitoring Health Contexts of Hajj Pilgrims from Wearable Sensor Data. 利用可穿戴传感器数据监测朝觐朝圣者健康状况的位置编码变压器。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Nazim A Belabbaci, Raphael Anaadumba, Mohammad Arif Ul Alam

引用次数: 0

Assessing Geographic Diversity in Systematic Reviews: A 3D Interactive Approach Using Cochrane SRs in IPF. 评估系统评价中的地理多样性：在IPF中使用Cochrane SRs的3D互动方法。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Hui Li, Jinlian Wang, Hongfang Liu

{"title":"Assessing Geographic Diversity in Systematic Reviews: A 3D Interactive Approach Using Cochrane SRs in IPF.","authors":"Hui Li, Jinlian Wang, Hongfang Liu","doi":"","DOIUrl":"","url":null,"abstract":"The Systematic reviews (SRs) for Idiopathic Pulmonary Fibrosis (IPF) play a crucial role in guiding evidence-based healthcare by synthesizing data across multiple studies. A key factor in ensuring the reliability and applicability of these reviews is the geographic diversity of the authors involved, as this can significantly influence the generalizability of findings. Traditional 2D maps used to visualize author locations often fall short in capturing the depth and regional disparities effectively, as overlapping points or dense clusters can obscure critical details, resulting in an incomplete view of geographic distribution. To address these limitations, this study introduces a novel approach that combines a 3D geographic map and a Temporal-Spatial Graph Attention Network (TS-GAT) to assess and visualize the geographic diversity of authors in SRs on IPF. The 3D visualization provides an enhanced, layered representation of author locations, revealing hidden regional disparities and biases. The TS-GAT captures both temporal and spatial relationships in the author collaboration network, allowing for deeper insights into the evolution of geographic representation over time. This integrated approach aims to uncover potential biases in global representation, offering a comprehensive understanding of the geographic spread and temporal trends in authorship within SRs, ultimately contributing to more balanced and inclusive evidence synthesis.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"290-299"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automating and Evaluating Large Language Models for Accurate Text Summarization Under Zero-Shot Conditions. 自动化和评估大型语言模型在零射击条件下准确的文本摘要。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Maria Priebe Mendes Rocha, Hilda B Klasky

{"title":"Automating and Evaluating Large Language Models for Accurate Text Summarization Under Zero-Shot Conditions.","authors":"Maria Priebe Mendes Rocha, Hilda B Klasky","doi":"","DOIUrl":"","url":null,"abstract":"Automated text summarization (ATS) is crucial for collecting specialized, domain-specific information. Zero-shot learning (ZSL) allows large language models (LLMs) to respond to prompts on information not included in their training, playing a vital role in this process. This study evaluates LLMs' effectiveness in generating accurate summaries under ZSL conditions and explores using retrieval augmented generation (RAG) and prompt engineering to enhance factual accuracy and understanding. We combined LLMs with summarization modeling, prompt engineering, and RAG, evaluating the summaries using the METEOR metric and keyword frequencies through word clouds. Results indicate that LLMs are generally well-suited for ATS tasks, demonstrating an ability to handle specialized information under ZSL conditions with RAG. However, web scraping limitations hinder a single generalized retrieval mechanism. While LLMs show promise for ATS under ZSL conditions with RAG, challenges like goal misgeneralization and web scraping limitations need addressing. Future research should focus on solutions to these issues.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"461-470"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning. 生物生物学- nlu：通过指令调谐实现更一般化的医学语言理解。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Yujuan Velvin Fu, Giridhar Kaushik Ramachandran, Namu Park, Kevin Lybarger, Fei Xia, Ozlem Uzuner, Meliha Yetisgen

{"title":"BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning.","authors":"Yujuan Velvin Fu, Giridhar Kaushik Ramachandran, Namu Park, Kevin Lybarger, Fei Xia, Ozlem Uzuner, Meliha Yetisgen","doi":"","DOIUrl":"","url":null,"abstract":"Large language models (LLMs) such as ChatGPT are fine-tuned on large and diverse instruction-following corpora, and can generalize to new tasks. However, those instruction-tuned LLMs often perform poorly in specialized medical natural language understanding (NLU) tasks that require domain knowledge, granular text comprehension, and structured data extraction. To bridge the gap, we: (1) propose a unified prompting format for 7 important NLU tasks, (2) curate an instruction-tuning dataset, MNLU-Instruct, utilizing diverse existing open-source medical NLU corpora, and (3) develop BioMistral-NLU, a generalizable medical NLU model, through fine-tuning BioMistral on MNLU-Instruct. We evaluate BioMistral-NLU in a zero-shot setting, across 6 important NLU tasks, from two widely adopted medical NLU benchmarks: BLUE and BLURB. Our experiments show that our BioMistral-NLU outperforms the original BioMistral, as well as the proprietary LLMs - ChatGPT and GPT-4. Our dataset-agnostic prompting strategy and instruction tuning step over diverse NLU tasks enhance LLMs' generalizability across diverse medical NLU tasks. Our ablation experiments show that instruction-tuning on a wider variety of tasks, even when the total number of training instances remains constant, enhances downstream zero-shot generalization.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"149-158"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs. 利用法学硕士增强文献挖掘和知识图谱在阿尔茨海默病研究中利用健康的社会决定因素。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Tianqi Shang, Shu Yang, Weiqing He, Tianhua Zhai, Dawei Li, Bojian Hou, Tianlong Chen, Jason H Moore, Marylyn D Ritchie, Li Shen

{"title":"Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs.","authors":"Tianqi Shang, Shu Yang, Weiqing He, Tianhua Zhai, Dawei Li, Bojian Hou, Tianlong Chen, Jason H Moore, Marylyn D Ritchie, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals' risks of developing Alzheimer's disease (AD) and related dementias. Nevertheless, the etiological mechanisms underlying such relationships remain largely unclear, mainly due to difficulties in collecting relevant information. This study presents a novel, automated framework that leverages recent advancements of large language model (LLM) and natural language processing techniques to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities extracted from the general-purpose knowledge graph PrimeKG. Utilizing graph neural networks, we performed link prediction tasks to evaluate the resultant SDoH-augmented knowledge graph. Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas, offering a new tool for exploring the impact of social determinants on health outcomes. Our code is available at: https://github.com/hwq0726/SDoHenPKG.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"491-500"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable Diagnosis Prediction through Neuro-Symbolic Integration. 神经符号整合的可解释诊断预测。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Qiuhao Lu, Rui Li, Elham Sagheb, Andrew Wen, Jinlian Wang, Liwei Wang, Jungwei W Fan, Hongfang Liu

{"title":"Explainable Diagnosis Prediction through Neuro-Symbolic Integration.","authors":"Qiuhao Lu, Rui Li, Elham Sagheb, Andrew Wen, Jinlian Wang, Liwei Wang, Jungwei W Fan, Hongfang Liu","doi":"","DOIUrl":"","url":null,"abstract":"Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable weights and thresholds. Our models, particularly Mmulti-pathway and Mcomprehensive, demonstrate superior performance over traditional models such as Logistic Regression, SVM, and Random Forest, achieving higher accuracy (up to 80.52%) and AUROC scores (up to 0.8457) in the case study of diabetes prediction. The learned weights and thresholds within the LNN models provide direct insights into feature contributions, enhancing interpretability without compromising predictive power. These findings highlight the potential of neuro-symbolic approaches in bridging the gap between accuracy and explainability in healthcare AI applications. By offering transparent and adaptable diagnostic models, our work contributes to the advancement ofprecision medicine and supports the development of equitable healthcare solutions. Future research will focus on extending these methods to larger and more diverse datasets to further validate their applicability across different medical conditions and populations.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"332-341"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0