Healthcare Informatics Research最新文献

Development and Validation of a Predictive AI Framework for Diabetic Foot Ulcer Monitoring and Severity Assessment: A Step towards Self-monitoring and Primary Care Integration. 糖尿病足溃疡监测和严重程度评估预测AI框架的开发和验证：迈向自我监测和初级保健整合的一步。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.69

Subodh S Satheesh, Akhila Rayampalli, Akash G Prabhune, Vinay R Sri Hari

{"title":"Development and Validation of a Predictive AI Framework for Diabetic Foot Ulcer Monitoring and Severity Assessment: A Step towards Self-monitoring and Primary Care Integration.","authors":"Subodh S Satheesh, Akhila Rayampalli, Akash G Prabhune, Vinay R Sri Hari","doi":"10.4258/hir.2026.32.1.69","DOIUrl":"10.4258/hir.2026.32.1.69","url":null,"abstract":"Objectives: Diabetic foot ulcer (DFU) is a critical complication of diabetes that can lead to severe outcomes such as infection, amputation, and increased mortality if left untreated. Early detection and continuous monitoring are essential but remain challenging, especially in resource-limited settings such as India. This study developed and validated a deep learning algorithm to classify diabetic foot images into severity grades based on the International Working Group on the Diabetic Foot classification: grade 0 (healthy), grade 1 (mild), grade 2 (moderate), and grade 3 (severe).Methods: A dataset of 407 clinical images was collected from open-source platforms and clinics in South India and expanded to 612 images through data augmentation. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Multiple machine learning models were tested, including MobileNet_V2, EfficientNet-b0, DenseNet121, ResNet_50, VGG16, and ViT_b_16.Results: Among the evaluated models, MobileNet_V2 demonstrated the highest validation accuracy (82%) and achieved an F1-score of 79% on the test set. Although the model showed strong training accuracy, minor overfitting was observed, particularly in distinguishing adjacent severity grades. To address this, dropout, batch normalization, and early stopping were employed. Overall, the model generalized well, showing high accuracy in detecting healthy cases and acceptable performance across ulcer severity grades.Conclusions: This study underscores the potential of machine learning-based tools to support frontline healthcare workers and facilitate patient self-monitoring in low-resource environments. Future work will focus on refining the model and integrating it into user-friendly applications.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"69-76"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179215","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

Artificial Intelligence Exhibits Critical Blind Spots in Allergy Test Room Safety Design. 人工智能在过敏试验室安全设计中出现关键盲点。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.109

Betul Dumanoglu, Cihangir Sahin, Ozge Can Bostan, Zeynep Gulec Koksal, Pamir Cerci

引用次数: 0

Maternal Coffee Consumption during Pregnancy and Risk of Allergic Diseases in Children: The Korean Children's Environmental Health Study. 孕妇在怀孕期间饮用咖啡与儿童过敏性疾病的风险：韩国儿童环境健康研究

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.77

Sharmin Afroz, Seoyeon Cho, Jongmin Oh, Jin-Hong Kim, Sung Yeon Kim, Eunhee Ha, Yi-Jun Kim

{"title":"Maternal Coffee Consumption during Pregnancy and Risk of Allergic Diseases in Children: The Korean Children's Environmental Health Study.","authors":"Sharmin Afroz, Seoyeon Cho, Jongmin Oh, Jin-Hong Kim, Sung Yeon Kim, Eunhee Ha, Yi-Jun Kim","doi":"10.4258/hir.2026.32.1.77","DOIUrl":"10.4258/hir.2026.32.1.77","url":null,"abstract":"Objectives: The effects of maternal coffee consumption during pregnancy on childhood allergic diseases (ADs) remain insufficiently established. This study aimed to investigate the association between maternal coffee consumption during pregnancy and the risk of ADs in offspring up to 36 months of age.Methods: We analyzed data from 3,252 mother-child pairs enrolled in the Korean Children's Environmental health Study (Ko-CHENS). Maternal coffee and caffeine intake were assessed using a food frequency questionnaire. Childhood ADs were identified based on caregiver reports of physician diagnoses. Cox proportional hazards models were applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), with adjustment for potential confounding factors.Results: Overall, two-thirds (67.5%) of children were reported to have at least one AD, with cumulative incidences at 36 months of age of 47.8% for atopic dermatitis, 23.9% for food allergy, 30.2% for allergic rhinitis, and 2.4% for asthma. Compared with no coffee intake, maternal coffee consumption of <1 serving/day was associated with a reduced risk of atopic dermatitis (HR = 0.89, 95% CI 0.81-0.99, p = 0.045) and food allergy (HR = 0.86, 95% CI 0.74-1.00, p = 0.061). Higher intake (≥1 serving/day) was significantly associated with a lower risk of food allergy (HR = 0.61, 95% CI 0.42-0.88, p = 0.009). No significant associations were observed for asthma or allergic rhinitis.Conclusions: Mild maternal coffee intake during pregnancy may be associated with a reduced risk of specific ADs in early childhood.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"77-91"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179178","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

Machine Learning for Predicting Coronary Heart Disease Risk in Patients with Hypertension: An Ensemble Modeling Approach. 预测高血压患者冠心病风险的机器学习：一种集成建模方法。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.28

Fadratul Hafinaz Hassan, Shuchen Wang, Alina Miron

{"title":"Machine Learning for Predicting Coronary Heart Disease Risk in Patients with Hypertension: An Ensemble Modeling Approach.","authors":"Fadratul Hafinaz Hassan, Shuchen Wang, Alina Miron","doi":"10.4258/hir.2026.32.1.28","DOIUrl":"10.4258/hir.2026.32.1.28","url":null,"abstract":"Objectives: This study aimed to develop an optimized ensemble learning model to improve the prediction of hypertension complicated by coronary heart disease (CHD) through advanced feature selection and classifier fusion, thereby enhancing both accuracy and stability in risk assessment.Methods: We constructed an ensemble-based predictive model using voting fusion to enhance early detection of hypertension complicated by CHD. The dataset comprised 2,487 patients with essential hypertension (EH) complicated by CHD and 3,904 non-CHD controls. Following data preprocessing procedures, including data cleaning and univariate and multivariate feature selection, an 18-dimensional feature set was derived. Five machine learning algorithms (logistic regression, random forest, XGBoost, CatBoost, and CART) were trained independently and subsequently integrated through a voting ensemble to optimize predictive performance.Results: The voting fusion model outperformed all individual classifiers, achieving an area under the curve of 0.906 and an accuracy of 0.888 in predicting EH complicated by CHD.Conclusions: The proposed ensemble model improves classification accuracy and robustness, offering a clinically useful tool for early risk stratification of hypertension-associated CHD. Although the model demonstrates strong predictive performance using cross-sectional data, its reliance on single-timepoint measurements and selected control populations necessitates further validation. Pending additional studies, this framework may serve as a supplementary decision-support tool within clinical informatics systems.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"28-37"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179138","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

Development and Evaluation of BABAT TB: A Smart System-Based Reminder Box for Enhancing Tuberculosis Medication Adherence. BABAT TB的开发和评估：一个基于智能系统的提醒盒，用于提高结核病药物依从性。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.4

Sri Ratna Rahayu, Anan Nugroho, Dina Nur Anggraini Ningrum, Aufiena Nur Ayu Merzistya, Tutuk Wijayantiningrum, Jhonatur Stheven Simanjuntak, Muhammad Zidan Maali, Kasyfil Aziz Hafidh, Annisa Putri Salsabila, Salsabila Kinaya Pranindita, Naufal Ilham Ramadhan

{"title":"Development and Evaluation of BABAT TB: A Smart System-Based Reminder Box for Enhancing Tuberculosis Medication Adherence.","authors":"Sri Ratna Rahayu, Anan Nugroho, Dina Nur Anggraini Ningrum, Aufiena Nur Ayu Merzistya, Tutuk Wijayantiningrum, Jhonatur Stheven Simanjuntak, Muhammad Zidan Maali, Kasyfil Aziz Hafidh, Annisa Putri Salsabila, Salsabila Kinaya Pranindita, Naufal Ilham Ramadhan","doi":"10.4258/hir.2026.32.1.4","DOIUrl":"10.4258/hir.2026.32.1.4","url":null,"abstract":"Objectives: This study aimed to develop and evaluate the functionality of a smart system-based prototype, \"BABAT TB,\" a medication box designed to assist tuberculosis (TB) patients in adhering to their treatment schedules.Methods: The development of the BABAT TB prototype followed the Design Science Research Methodology framework, encompassing the stages of problem identification and motivation, defining the objectives for a solution, and system design and development. Problem identification and motivation were established through semi-structured interviews with TB program officers and document analysis. The prototype integrates two main functional components: a drug quantity monitoring module and a reminder/alarm system for medication schedules, both monitored in real time. Serial communication through a SIM register is used to transmit real-time drug quantity data to the associated application. The system is powered by two 4,000 mAh lithium batteries, providing up to 2 months of use without recharging.Results: The prototype consists of three core hardware components: the input control circuit, the timer circuit, and the drug amount detection circuit. All modules were successfully assembled and powered. The timer was configured according to medical prescriptions, and the alarm activated at the scheduled times, effectively reminding patients to take their medication.Conclusions: The BABAT TB prototype effectively measures medication quantities and provides timely alerts, thereby supporting adherence to TB treatment. In addition, it can transmit data related to drug quantities, consultation schedules, and prototype identity cards (IDs) to a database.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"4-13"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179147","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

Survival Period Prediction in Cervical Cancer Patients Using the Selective Stacking Technique. 应用选择性叠加技术预测宫颈癌患者生存期。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.38

Intorn Chanudom, Ekkasit Tharavichitkul, Wimalin Laosiritaworn

{"title":"Survival Period Prediction in Cervical Cancer Patients Using the Selective Stacking Technique.","authors":"Intorn Chanudom, Ekkasit Tharavichitkul, Wimalin Laosiritaworn","doi":"10.4258/hir.2026.32.1.38","DOIUrl":"10.4258/hir.2026.32.1.38","url":null,"abstract":"Objectives: This study aims to increase the effectiveness of cervical cancer treatment by developing a survival prediction model using an innovative ensemble machine learning approach, namely the selective stacking technique.Methods: Patient data obtained from the Faculty of Medicine, Chiang Mai University, Thailand, were utilized to validate the real-world applicability of the proposed approach. The selective stacking model employed a two-stage machine learning framework in which outputs from base machine learning models were systematically combined through meta-level learning. Importantly, the performance of the proposed model was compared with that reported in previous studies that relied on individual machine learning algorithms as baselines. To provide deeper insight into the predictive mechanisms of the model, local interpretable model-agnostic explanations were applied to assess feature importance and identify the most influential factors contributing to model predictions.Results: The classification model developed using the selective stacking technique demonstrated a marked improvement in prediction accuracy, achieving an accuracy of 91.41%. The regression model also showed robust performance, with a root mean square error of 18.92 and an r value of 0.669. Feature importance analysis indicated that side effect status involving surrounding organs emerged as the most influential factor in survival prediction.Conclusions: The selective stacking model exhibited superior predictive performance compared with the base models, suggesting that this approach offers a promising strategy for cervical cancer survival prediction and may support the development of more personalized treatment planning.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"38-49"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179219","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

Review of the 2025 Fall Conference of the Korean Society of Medical Informatics: Generative AI in Healthcare Systems-From Insight to Impact. 韩国医学信息学学会2025年秋季会议综述：医疗保健系统中的生成人工智能——从洞察到影响。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.1

Jooyun Lee, Younghee Lee, Seo Yeon Baik, Jisan Lee, Seung-Bo Lee, Jungchan Park, Hyekyung Woo

引用次数: 0

Educational Needs and Level of Knowledge in Standard Healthcare Terminology Use in Korea: A Cross-Sectional Survey. 韩国标准医疗术语使用的教育需求和知识水平：一项横断面调查。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.50

Ahjung Byun, Hyeoun-Ae Park, Jiyeon Yu, Sumi Sung

{"title":"Educational Needs and Level of Knowledge in Standard Healthcare Terminology Use in Korea: A Cross-Sectional Survey.","authors":"Ahjung Byun, Hyeoun-Ae Park, Jiyeon Yu, Sumi Sung","doi":"10.4258/hir.2026.32.1.50","DOIUrl":"10.4258/hir.2026.32.1.50","url":null,"abstract":"Objectives: This study aimed to investigate the experience, knowledge, and educational needs regarding standard healthcare terminologies-specifically Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and the International Classification of Diseases/Korean Classification of Diseases (ICD/KCD)-among professionals in the clinical, industrial, and academic sectors in Korea.Methods: A descriptive survey was conducted using an online questionnaire distributed between November 21 and December 5, 2023. The questionnaire included items on participants' experiences with, self-reported knowledge of, and educational needs for standard terminologies. A total of 325 responses were analyzed.Results: Participants reported the highest levels of experience and knowledge with ICD/KCD, whereas knowledge of SNOMED CT and LOINC was relatively low. Statistically significant differences in knowledge were observed across professional groups (p < 0.05), with terminology experts reporting higher levels than others. Educational needs were greatest for ICD/KCD and SNOMED CT, particularly in data collection and use case application. The most frequently cited barriers to adopting standard terminologies were a lack of training programs, the cost and time required for training, and the structural complexity of the terminologies.Conclusions: These findings underscore the importance of customized and systematic educational strategies to promote the use of standard terminologies. Policy-level support, standardized training materials, and the preparation of qualified trainers are essential to enhance semantic interoperability and data utilization in Korean healthcare.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"50-58"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179131","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

Nurses' Perceptions and Utilization Plans for Applying Companion Robots to Acute Stroke Patient Care: A Delphi Study. 护理人员对伴侣机器人应用于急性脑卒中患者护理的认知和使用计划：德尔菲研究。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.59

Hee-Jin Choo, Sun-Mi Lee

{"title":"Nurses' Perceptions and Utilization Plans for Applying Companion Robots to Acute Stroke Patient Care: A Delphi Study.","authors":"Hee-Jin Choo, Sun-Mi Lee","doi":"10.4258/hir.2026.32.1.59","DOIUrl":"10.4258/hir.2026.32.1.59","url":null,"abstract":"Objectives: This study aimed to explore nurses' perceptions of, and utilization plans for, companion robots to support the physical and mental health of patients with acute stroke. In addition, the study sought to provide foundational data for the development of companion robots tailored to acute stroke patients. It also investigated obstructive factors and potential solutions to difficulties encountered when applying companion robots in the care of patients with acute stroke.Methods: Using the Delphi technique, this study surveyed 14 nurses working in the neurology ward and stroke intensive care unit of a tertiary hospital in Seoul across three survey rounds.Results: After completion of the three Delphi survey rounds, Cronbach's α was 0.78, and stability values were all below 0.5; therefore, no additional rounds were conducted. A total of 54 items were finally selected, including 10 items related to educational aspects for nurses and patients, 12 items addressing impacts on nurses and patients, 19 items describing companion robot functions required for stroke patients, and 13 items identifying the most appropriate design elements.Conclusions: Companion robots are expected to contribute to the physical and emotional care of patients with acute stroke admitted to tertiary hospitals by functioning as a nursing intervention, while also reducing nurses' workload, improving the quality of nursing care, and supporting patient safety management. In addition, efforts should be made to ensure the harmonious control and utilization of newly developed robots and to strengthen robot-related job competencies among nurses.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"59-68"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179155","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

Engaging with Facebook Health Support Groups among Australian Culturally and Linguistically Diverse Populations. 在澳大利亚文化和语言多样化的人群中与Facebook健康支持小组进行接触。

IF 2.1

Healthcare Informatics Research Pub Date : 2026-01-01 Epub Date: 2026-01-31 DOI: 10.4258/hir.2026.32.1.14

Mohamad Mahmoud Al Zein, Khin Than Win, Ibrahim Alananzeh

{"title":"Engaging with Facebook Health Support Groups among Australian Culturally and Linguistically Diverse Populations.","authors":"Mohamad Mahmoud Al Zein, Khin Than Win, Ibrahim Alananzeh","doi":"10.4258/hir.2026.32.1.14","DOIUrl":"10.4258/hir.2026.32.1.14","url":null,"abstract":"Objectives: This study aims to explore the key factors that enhance engagement in Facebook health support groups among Australian culturally and linguistically diverse (CALD) communities.Methods: A cross-sectional online survey was conducted using convenience sampling. A total of 1,145 CALD participants residing in New South Wales, Australia, were initially recruited. From this sample, 150 participants who self-reported regular engagement with Facebook health support groups were included in the final analysis. A pilot test (n = 30) demonstrated strong internal consistency (Cronbach's alpha >0.70). Data collection involved a structured questionnaire employing a 7-point Likert scale to assess factors such as motivation, trust, perceived support (received and given), social connectedness, and sense of virtual community.Results: Motivation and trust significantly influenced both support dynamics and the perceived sense of virtual community. The sense of virtual community, in turn, strongly predicted engagement in Facebook health support groups. Interestingly, social connectedness alone was not a significant predictor of engagement.Conclusions: Fostering a strong sense of virtual community appears to be a critical factor in encouraging sustained engagement in digital health platforms among CALD populations.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"14-27"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179143","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