BMC Medical Informatics and Decision Making最新文献

{"title":"Machine learning in the prediction of diabetic peripheral neuropathy: a systematic review.","authors":"Yueying Ma, Zhiying Wang, Zheng Yao, Bin Lu, Yanming He","doi":"10.1186/s12911-025-03201-6","DOIUrl":"10.1186/s12911-025-03201-6","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"344"},"PeriodicalIF":3.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191323","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":"High-throughput biomedical relation extraction for semi-structured web articles empowered by large language models.","authors":"Songchi Zhou, Sheng Yu","doi":"10.1186/s12911-025-03204-3","DOIUrl":"10.1186/s12911-025-03204-3","url":null,"abstract":"<p><strong>Background: </strong>We aim to develop a high-throughput biomedical relation extraction system tailored for semi-structured biomedical websites, leveraging the reading comprehension abilities and domain-specific medical knowledge of large language models (LLMs).</p><p><strong>Methods: </strong>We formulate relation extraction as a series of binary classification problems. Given the context from semi-structured biomedical web articles, LLMs decide whether a relation holds while providing accompanying rationales for factual verification. The article's main title is designated as the tail entity, and candidate head entities are identified by matching against a biomedical thesaurus with semantic typing to guide candidate relation types. To assess system performance and robustness, we compare general-purpose, domain-adapted, and parameter-efficient LLMs on an expert-curated benchmark, evaluating their relative effectiveness in extracting relations from semi-structured biomedical websites.</p><p><strong>Results: </strong>Domain-adapted models consistently outperform their general-purpose counterparts. Specifically, MedGemma-27B achieves an F1 score of 0.820 and Cohen's Kappa of 0.677, representing clear improvements over its base model Gemma3-27B (F1 = 0.771, Kappa = 0.604). Notably, MedGemma-27B also surpasses OpenAI's GPT-4o (F1 = 0.708, Kappa = 0.561) and GPT-4.1 (F1 = 0.732, Kappa = 0.597), demonstrating the advantage of biomedical domain adaptation even over stronger proprietary models. Among all evaluated models, DeepSeek-V3 yields the best overall performance (F1 = 0.844, Kappa = 0.730). Using MedGemma-27B, we extracted 225,799 relation triplets across three relation types from three authoritative biomedical websites. Case studies further highlight both the strengths and persistent challenges of different LLM classes in biomedical relation extraction from semi-structured content.</p><p><strong>Conclusion: </strong>Our study demonstrates that LLMs can serve as effective engines for high-throughput biomedical relation extraction, with domain-adapted and parameter-efficient models offering practical advantages. The framework is scalable and broadly adaptable, enabling efficient extraction of diverse biomedical relations across heterogeneous semi-structured websites. Beyond technical performance, the ability to extract reliable biomedical relations at scale can directly benefit clinical applications, such as enriching biomedical knowledge graphs, supporting evidence-based guideline development, and ultimately assisting clinicians in accessing structured medical knowledge for decision-making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"351"},"PeriodicalIF":3.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191358","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":"Simple but complex: aged care healthcare professionals' perspectives on the design of a digital falls dashboard.","authors":"Laura Dodds, Isabelle Meulenbroeks, S Sandun Malpriya Silva, Kristiana Ludlow, Crisostomo Mercado, Karla Seaman, Nasir Wabe, Melissa Baysari, Johanna I Westbrook, Amy D Nguyen","doi":"10.1186/s12911-025-03135-z","DOIUrl":"10.1186/s12911-025-03135-z","url":null,"abstract":"<p><strong>Background: </strong>Digital dashboards are widely employed across healthcare settings to present data, supporting timely risk identification and enhancing clinical decision-making. Incorporating feedback from end-users into dashboard design supports their uptake and utilisation. The current study aimed to: (a) understand how healthcare professionals working in residential aged care gather, interpret, transfer and communicate clinical information especially for falls management; and (b) utilise co-design methods to determine healthcare professionals' preferences for presentation, content and functionality of a digital falls dashboard to support delivery of care in residential aged care.</p><p><strong>Methods: </strong>Participants were recruited via aged care provider and primary health network contacts. Individual interviews with general practitioners (GPs) (n = 3) explored end-user needs including information needs for falls management, decision-making processes, and dashboard preferences. Dashboard prototypes were developed using the interview findings and published guidelines. Prototypes were then presented for feedback in eight workshops (n = 20 participants; residential aged care staff, GPs, and geriatricians) completed via videoconferencing or in-person to gain feedback. Interview and workshop transcripts were analysed using template analysis.</p><p><strong>Results: </strong>During interviews, GPs discussed difficulties in accessing aged care resident information, clinical decision-making in residential aged care, and use of decision support. During workshops, healthcare professionals shared feedback on the design, content, and functionality of dashboard prototypes. Healthcare professionals also discussed themes of human-technology interaction. This included mistrust of new digital tools and barriers to their use in residential aged care. The current study found that healthcare professionals want a dashboard that displays relevant resident data, such as medications, includes features for benchmarking, and provides detailed insights to support decision making. They expressed a need for evidence-based decision support but advocated for minimal alerts.</p><p><strong>Conclusions: </strong>Healthcare professionals were receptive to using a dashboard in residential aged care to minimise resident falls. They shared their design ideas in co-design interviews and workshops for a prospective dashboard. Findings informed the initial development and subsequent revisions of the dashboard to align with end-user preferences.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"347"},"PeriodicalIF":3.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191292","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":"Evaluating the effectiveness of a risk prediction model (PERSARC) on improving treatment decisions quality for patients with soft-tissue sarcomas: the VALUE-PERSARC study.","authors":"Anouk A Kruiswijk, Perla J Marang-van de Mheen, Lisa A E Vlug, Ellen G Engelhardt, Marta Fiocco, Rick L Haas, Yvonne M Schrage, Cornelis Verhoef, Marc H A Bemelmans, Robert J van Ginkel, Johannes J Bonenkamp, Arjen J Witkamp, Michiel A J van de Sande, Leti van Bodegom-Vos","doi":"10.1186/s12911-025-03166-6","DOIUrl":"10.1186/s12911-025-03166-6","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"345"},"PeriodicalIF":3.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191325","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":"Evaluating the performance of ChatGPT in clinical multidisciplinary treatment: a retrospective study.","authors":"Xueqi Wang, Jianhua Guo, Tao Zhang, Huajun Lu, Dandan Zhou, Haitao Zhang, Xuebin Wang","doi":"10.1186/s12911-025-03181-7","DOIUrl":"10.1186/s12911-025-03181-7","url":null,"abstract":"<p><strong>Background: </strong>Multidisciplinary treatment (MDT) consultations are essential for managing complex patients. However, resource and time constraints can limit their quality. Large language models (LLMs) have shown potential in assisting clinical decision-making, but their performance in complex MDT scenarios remains unclear. This study aims to evaluate the quality of MDT recommendations generated by ChatGPT compared to those provided by physicians.</p><p><strong>Methods: </strong>Clinical data from 64 patient cases were retrospectively included in the study. ChatGPT was asked to provide specific MDT recommendations. 2 experienced physicians evaluated and scored the responses in a blinded manner across 5 aspects: comprehensiveness, accuracy, feasibility, safety, and efficiency, each assessed by 2 questions.</p><p><strong>Results: </strong>The median overall score for ChatGPT was 41.0 out of 50.0, which was lower than the MDT physicians' median score of 43.5 (p = 0.001). Compared to the MDT physicians' responses, ChatGPT excelled in comprehensiveness (p < 0.001) but fell short in accuracy (p < 0.001), feasibility (p < 0.001), and efficiency (p = 0.003). Analysis of specific questions revealed that ChatGPT lacked the ability to reason through the etiologies of complex cases.</p><p><strong>Conclusion: </strong>This study indicates that ChatGPT has potential in clinical MDT applications, particularly in demonstrating more comprehensive consideration of clinical factors. However, ChatGPT still has deficiencies in accuracy, which could lead to incorrect healthcare decisions. Therefore, further development and clinical validation of LLMs are necessary. Recognizing the current limitations of LLMs, it is essential to use them with caution in clinical practice.</p><p><strong>Trial registration: </strong>Not applicable to the present retrospective study. For transparency, a related prospective extension is registered at ChiCTR (ChiCTR2400088563; registered on 21 August 2024).</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"340"},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173759","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":"The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study.","authors":"Rildo Pinto da Silva, Antonio Pazin-Filho","doi":"10.1186/s12911-025-03178-2","DOIUrl":"10.1186/s12911-025-03178-2","url":null,"abstract":"<p><strong>Background: </strong>While it is advocated that the use of unstructured data extracted from medical records is important for enhancing machine learning models, few studies have evaluated whether this occurs. A retrospective, head-to-head comparative study was conducted to evaluate machine learning models for in-hospital mortality prediction. The study assessed and quantified the potential performance improvement resulting from the inclusion of unstructured data.</p><p><strong>Methods: </strong>Hospitalizations of patients with a confirmed COVID-19 diagnosis at a tertiary teaching hospital specialized in emergency care were selected (n = 844). For the models with structured data, 21 variables were selected from laboratory tests and patient monitoring. For the hybrid models, an additional 21 clinical assertions (e.g., \"has_symptom affirmed dyspnea\") were included. Six models with the best discriminative performance out of 11 trained and validated were selected for the testing phase. The most representative variables were evaluated using an explainable artificial intelligence model.</p><p><strong>Results: </strong>The random forest model demonstrated the highest performance, achieving an area under the receiver operating characteristic curve (AUC ROC) of 0.9260, an increase from 0.9170 when using only structured data. The inclusion of unstructured data also improved sensitivity from 0.8108 to 0.8378 while specificity was maintained at 0.8667. However, these performance improvements were not found to be statistically significant different from models with only structured data.</p><p><strong>Conclusion: </strong>The study concluded that the inclusion of unstructured data did not increase the predictive power of machine learning models for COVID-19 mortality. It was also determined that human involvement is crucial for implementation, specifically for validating natural language processing (NLP) outputs and tailoring the selection of unstructured features, given the inherent challenges in processing such data.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"333"},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173781","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":"ProtoMAP: prototypical network based few-shot learning for missed abortion prediction.","authors":"Xiaoli Bo, Lu You, GuoYing Li, Xiwen Yang, Jun Zhang, Xun Deng","doi":"10.1186/s12911-025-03171-9","DOIUrl":"10.1186/s12911-025-03171-9","url":null,"abstract":"<p><p>Missed abortion is a prevalent issue in clinical practice, posing both physical risks to the mother and substantial psychological impact. Accurately predicting the risk of missed abortion is essential for guiding timely clinical interventions and safeguarding maternal health. Data on missed abortion are scarce and imbalanced. Given the limited clinical data and the nonlinear interrelationships among multiple features, traditional machine learning methods often fail to capture essential patterns, thereby their prediction performance is suboptimal. This paper proposes a prototype network based on few-shot learning, namely ProtoMAP. The goal is to train a missed abortion prediction model using a limited number of samples, while achieving performance comparable to models trained on large-scale datasets. Unlike previous studies, this work is the first to explore the problem of missed abortion prediction using a few-shot learning approach. A series of experiments were conducted, and the results demonstrate that the proposed ProtoMAP model significantly outperforms a range of baseline models in the task of missed abortion prediction. These results demonstrate that ProtoMAP not only supports missed abortion prediction in a few-shot learning setting, but also achieves performance that rivals or exceeds that of baseline models trained with overall data. And it demonstrates the practical utility of ProtoMAP for clinical missed abortion prediction, particularly in scenarios where data is scarce.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"336"},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173796","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":"CADxPolydetect: a clinically explainable hybrid deep learning system for multi-class colorectal lesion detection using augmented colonoscopy images.","authors":"Akella S Narasimha Raju, Shaik Jakeer Hussain, M Rajababu, Ranjith Kumar Gatla, K Venkatesh, Venkataramana Guntreddi","doi":"10.1186/s12911-025-03176-4","DOIUrl":"10.1186/s12911-025-03176-4","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"335"},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173679","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":"Developing an interpretable machine learning model for easily detecting insulin resistance among breast cancer survivors: a cross-sectional study.","authors":"Mengxia Fu, Zhiming Peng, Xue Yu, Dapeng Lv, Min Wu","doi":"10.1186/s12911-025-03189-z","DOIUrl":"10.1186/s12911-025-03189-z","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate a classification model for insulin resistance in female individuals who have survived breast cancer using easily obtainable clinical and demographic features.</p><p><strong>Methods: </strong>Data were obtained from the U.S. National Health and Nutrition Examination Survey (NHANES) spanning 1999 to March 2020. A total of 340 female individuals who have survived breast cancer were included, and participants were randomly assigned to a training set (n = 239) and a testing set (n = 101). Multiple machine learning algorithms were trained, including Logistic Regression, Random Forest, and Support Vector Machine. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).</p><p><strong>Results: </strong>All models demonstrated strong classification performance in the testing set, with AUC values exceeding 0.87. Among them, the Random Forest and Support Vector Machine models showed superior performance in DCA. Of the seven input features-body mass index, fasting blood glucose, triglyceride, HDL cholesterol, poverty income ratio, race, and education-fasting blood glucose had the highest positive feature importance for classifying insulin resistance.</p><p><strong>Conclusions: </strong>This study demonstrates the feasibility of using machine learning algorithms to accurately predict insulin resistance in individuals who have survived breast cancer with a limited set of clinical and demographic variables. The Random Forest and Support Vector Machine models, in particular, offer strong classification performance and may support clinicians in early identification and management of insulin resistance among individuals in this high-risk population.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"341"},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173744","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":"Computational definition of medical exclusion and feasibility of excluding people not eligible for French population-based colorectal cancer screening from the French medico-administrative database.","authors":"Akoï Koïvogui, Christian Balamou, Robert Benamouzig, Catherine Duclos","doi":"10.1186/s12911-025-03175-5","DOIUrl":"10.1186/s12911-025-03175-5","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In the French population-based colorectal cancer screening program (CRCSP), the fact that the medical-exclusion rate was estimated only after a collection of voluntary statements from subjects could compromise an exhaustive collection of potential cases of medical-exclusion. The health insurance medico-administrative database (SNDS) that contains medical and healthcare consumption information have to date never been used to refine the target population of the CRCSP.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To identify in the SNDS, from published and disparate algorithms, the computational definitions of morbid situations that could justify medical exclusion from the CRCSP.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The non-systematic review of the literature synthetised an exhaustive list of algorithms targeting in SNDS, the morbid situations (CCR, colorectal adenoma/polyp, chronic inflammatory bowel disease, familial adenomatous polyposis, or Lynch syndrome colonoscopy, coloscanner, polypectomy) which may justify temporary or permanent medical exclusion from the CRCSP campaigns. Secondly, the discovered codes of morbid situations were searched on statistical reports to estimate their frequencies of use in SNDS (in 2021), and their interest in the computational phenotypes' algorithm.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The analysis of the literature (28 articles/studies) highlights the existence of diagnostic or therapeutic codes that can define in the SNDS database, the morbid situations justifying medical exclusion from the CRCSP. Except for personal or family history of CRC classifiable in the Z85.0 or Z80.0 codes of the ICD-10, almost all the morbid situations have a requestable definition in the SNDS. The target favoured by the search algorithms was the ICD-10 code (i.e., C18-C20, K50, K51). The definition codes listed were frequently used in SNDS in 2021, except for a few codes (D12.6 + 6, M07.5). From this definition of morbid situations by the only codes of the ICD-10 or the procedure codes emerges a feasibility and a decision-making algorithm for the choice of the person to be excluded from CRCSP campaign, using the SNDS. Age is the first discriminating variable in this decision-making algorithm because the CRCSP targeted people aged 50 to 74 years old and a restriction on age was made in several included SNDS's studies. The second discrimination based on diagnostic evidence derives its relevance from the quasi-systematic search for ICD-10 diagnostic codes in SNDS's studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;In addition to being widely used in the context of medico-economic and epidemiological studies, the SNDS currently contains almost all the data essential for estimating the rate of medical-exclusion during colorectal cancer screening campaigns. While initiating the answer to the question of the choice of the most appropriate algorithm in each context, this review of the literature also emphasizes the need for validation st","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"334"},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173723","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}