ACM transactions on computing for healthcare最新文献

Fetal pHocus: A Novel Approach to Non-Invasive Fetal Arterial Blood pH Assessment via Near-Infrared Spectroscopy. 胎儿pHocus：一种通过近红外光谱无创胎儿动脉血pH值评估的新方法。

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ACM transactions on computing for healthcare Pub Date : 2026-04-01 DOI: 10.1145/3785412

Randall Fowler, Begum Kasap, Weitai Qian, Rishad Joarder, Kourosh Vali, Siddharth Mani, Herman L Hedriana, Aijun Wang, Diana Farmer, Soheil Ghiasi

{"title":"Fetal pHocus: A Novel Approach to Non-Invasive Fetal Arterial Blood pH Assessment via Near-Infrared Spectroscopy.","authors":"Randall Fowler, Begum Kasap, Weitai Qian, Rishad Joarder, Kourosh Vali, Siddharth Mani, Herman L Hedriana, Aijun Wang, Diana Farmer, Soheil Ghiasi","doi":"10.1145/3785412","DOIUrl":"10.1145/3785412","url":null,"abstract":"Modern intrapartum fetal health assessments are currently limited to monitoring heart rate and spatial parameters, neglecting critical biomarkers that remain unmeasurable with today's clinical devices without performing surgery. Without precise evaluations of oxygen levels and blood acidity, clinicians are forced to rely on postnatal assessments to gauge fetal well-being, a delay that may obscure timely intervention. Fetal blood pH is a vital indicator of acid-base balance and cellular health, as any deviation could indicate potential health risks such as hypoxia and acidemia. In this study, we leverage the indirect relationship between pH and oxygen saturation to estimate fetal blood pH non-invasively using near-infrared (NIR) spectroscopy with wavelengths optimized for light transmission depth and oxygen saturation measurements. A convolutional neural network (CNN) extracts features from the acquired data, enabling accurate prediction of fetal blood pH using our machine learning (ML) model. Evaluation using hypoxic sheep models demonstrated an average prediction error of just 0.023 pH units, with all rounds maintaining errors below 0.05 pH units.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 2","pages":""},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12994387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482304","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

HDFusion: Hierarchical Data Fusion for Robust Fetal Heart Rate Estimation using Transabdominal PPG Signals. HDFusion：利用经腹PPG信号进行稳健胎儿心率估计的分层数据融合。

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ACM transactions on computing for healthcare Pub Date : 2026-04-01 Epub Date: 2026-03-17 DOI: 10.1145/3785410

Tailai Lihe, Begum Kasap, Kourosh Vali, Soheil Ghiasi

{"title":"HDFusion: Hierarchical Data Fusion for Robust Fetal Heart Rate Estimation using Transabdominal PPG Signals.","authors":"Tailai Lihe, Begum Kasap, Kourosh Vali, Soheil Ghiasi","doi":"10.1145/3785410","DOIUrl":"10.1145/3785410","url":null,"abstract":"Evaluation of fetal health during pregnancy is highly dependent on monitoring of fetal heart rate (FHR). New technologies emerge, such as the transabdominal fetal pulse oximeter (TFO), a non-invasive, light-based measurement device, to provide obstetricians with additional fetal physiological markers such as fetal oxygen saturation. Estimation of FHR from TFO's acquired photoplethysmogram (PPG) signals is necessary for deriving oxygen saturation. Non-invasive optical sensing of deep fetal tissue is inherently challenged by low signal-to-noise ratio, and unpredictable anatomical and physiological dynamics, which render a particular sensor design suboptimal. Multiple sensors can conceptually enable the system to operate more robustly under such dynamics, assuming the data acquired by different sensors can be adaptively integrated to form a coherent view of the tissue. In this paper, we present an algorithm for data fusion at several levels of information abstraction, raw data, feature, and decision levels, to improve FHR estimation. We validate the proposed technique via in-vivo data collected in gold-standard pregnant ewe experiments using TFO. The root-mean-squared error of our three-level hierarchical data fusion compared to a single-level and two-level fusion improved by over 59% and 51%, respectively. This underscores the robustness of our approach in overcoming optical deep tissue sensing challenges.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 2","pages":""},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13038302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596587","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

Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data. 多地点纵向试验数据的LASSO联合Choquet回归预测结果。

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ACM transactions on computing for healthcare Pub Date : 2026-01-01 Epub Date: 2026-01-14 DOI: 10.1145/3761824

Semyon Lomasov, Hua Fang, Honggang Wang

{"title":"Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data.","authors":"Semyon Lomasov, Hua Fang, Honggang Wang","doi":"10.1145/3761824","DOIUrl":"10.1145/3761824","url":null,"abstract":"Aggregating person-level data across multiple clinical study sites is often constrained by privacy regulations, necessitating the development of decentralized modeling approaches in biomedical research. To address this requirement, a federated nonlinear regression algorithm based on the Choquet integral has been introduced for outcome prediction. This approach avoids reliance on prior statistical assumptions about data distribution and captures feature interactions, reflecting the non-additive nature of biomedical data characteristics. This work represents the first theoretical application of Choquet integral regression to multisite longitudinal trial data within a federated learning framework. The Multiple Imputation Choquet Integral Regression with LASSO (MIChoquet-LASSO) algorithm is specifically designed to reduce overfitting and enable variable selection in federated learning settings. Its performance has been evaluated using synthetic datasets, publicly available biomedical datasets, and proprietary longitudinal randomized controlled trial data. Comparative evaluations were conducted against benchmark methods, including OLS regression and Choquet OLS regression, under various scenarios such as model misspecification and both linear and nonlinear data structures in non-federated and federated contexts. MSE was used as the primary performance metric. Results indicate that MIChoquet-LASSO outperforms compared models in handling nonlinear longitudinal data with missing values, particularly in scenarios prone to overfitting. In federated settings, Choquet OLS underperforms, whereas the federated variant of the model, FEDMIChoquet-LASSO, demonstrates consistently better performance. These findings suggest that FEDMIChoquet-LASSO offers a reliable solution for outcome prediction in multisite longitudinal trials, addressing challenges such as missing values, nonlinear relationships, and privacy constraints while maintaining strong performance within the federated learning framework.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13052512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635245","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

Domain-Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging. 自动睡眠分期的域不变表示学习和睡眠动力学建模。

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ACM transactions on computing for healthcare Pub Date : 2025-10-01 Epub Date: 2025-10-13 DOI: 10.1145/3757066

Seungyeon Lee, Thai-Hoang Pham, Zhao Cheng, Ping Zhang

{"title":"Domain-Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging.","authors":"Seungyeon Lee, Thai-Hoang Pham, Zhao Cheng, Ping Zhang","doi":"10.1145/3757066","DOIUrl":"10.1145/3757066","url":null,"abstract":"Sleep staging has become a critical task in diagnosing and treating sleep disorders to prevent sleep-related diseases. With growing large-scale sleep databases, significant progress has been made toward automatic sleep staging. However, previous studies face critical problems in sleep studies; the heterogeneity of subjects' physiological signals, the inability to extract meaningful information from unlabeled data to improve predictive performances, the difficulty in modeling correlations between sleep stages, and the lack of an effective mechanism to quantify predictive uncertainty. In this study, we propose a neural network-based sleep staging model, DREAM, to learn domain generalized representations from physiological signals and model sleep dynamics. DREAM learns sleep-related and subject-invariant representations from diverse subjects' sleep signals and models sleep dynamics by capturing interactions between sequential signal segments and between sleep stages. We conducted a comprehensive empirical study to demonstrate the superiority of DREAM, including sleep stage prediction experiments, a case study, the usage of unlabeled data, and uncertainty. Notably, the case study validates DREAM's ability to learn the generalized decision function for new subjects, especially in case there are differences between testing and training subjects. Uncertainty quantification shows that DREAM provides prediction uncertainty, making the model reliable and helping sleep experts in real-world applications.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 4","pages":""},"PeriodicalIF":8.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12629632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566538","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

Video-based Intake Gesture Recognition Using Meal-length Context. 基于视频的进食手势识别。

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ACM transactions on computing for healthcare Pub Date : 2025-04-01 Epub Date: 2025-02-17 DOI: 10.1145/3709151

Zeyu Tang, Adam Hoover

{"title":"Video-based Intake Gesture Recognition Using Meal-length Context.","authors":"Zeyu Tang, Adam Hoover","doi":"10.1145/3709151","DOIUrl":"10.1145/3709151","url":null,"abstract":"This article explores video analysis methods for monitoring eating behaviors, a critical factor in approximately 70% of global deaths due to illnesses like cancer, diabetes, and heart disease. Automated monitoring quantifies aspects such as meal duration, food types, and intake gestures (bite and drink gestures). Previous deep-learning methods segment videos into short clips (e.g., 16 frames at 8 Hz) for analysis, but this approach overlooks common meal-length patterns in gesture distribution across different individuals and sessions, which can enhance detection accuracy. Our study introduces a novel pipeline that analyzes the entire meal context (5-40 minutes). We propose a framework allowing a global detector to learn meal-length patterns with manageable computational demands. Additionally, we introduced a new augmentation technique to generate hundreds of meal-length feature samples per video, facilitating effective training of a global detector with limited video availability. Experimental results on two datasets (Clemson Cafeteria and EatSense) demonstrate that our pipeline significantly enhances the performance of state-of-the-art window-based networks, particularly in reducing false positives in gesture detection. On the Clemson Cafeteria dataset of 486 meal videos (the largest dataset to date), our method achieves F1 scores of 0.93 for bite gestures and 0.88 for drink gestures, substantially outperforming existing methodologies.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 2","pages":""},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12872170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127763","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

CogProg: Utilizing Large Language Models to Forecast In-the-moment Health Assessment. CogProg：利用大型语言模型预测即时健康评估。

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ACM transactions on computing for healthcare Pub Date : 2025-04-01 Epub Date: 2025-04-24 DOI: 10.1145/3709153

Gina Sprint, Maureen Schmitter-Edgecombe, Raven Weaver, Lisa Wiese, Diane J Cook

{"title":"CogProg: Utilizing Large Language Models to Forecast In-the-moment Health Assessment.","authors":"Gina Sprint, Maureen Schmitter-Edgecombe, Raven Weaver, Lisa Wiese, Diane J Cook","doi":"10.1145/3709153","DOIUrl":"10.1145/3709153","url":null,"abstract":"Forecasting future health status is beneficial for understanding health patterns and providing anticipatory support for cognitive and physical health difficulties. In recent years, generative large language models (LLMs) have shown promise as forecasters. Though not traditionally considered strong candidates for numeric tasks, LLMs demonstrate emerging abilities to address various forecasting problems. They also provide the ability to incorporate unstructured information and explain their reasoning process. In this paper, we explore whether LLMs can effectively forecast future self-reported health state. To do this, we utilized in-the-moment assessments of mental sharpness, fatigue, and stress from multiple studies, utilizing daily responses (N=106 participants) and responses that are accompanied by text descriptions of activities (N=32 participants). With these data, we constructed prompt/response pairs to predict a participant's next answer. We fine-tuned several LLMs and applied chain-of-thought prompting evaluating forecasting accuracy and prediction explainability. Notably, we found that LLMs achieved the lowest mean absolute error (MAE) overall (0.851), while gradient boosting achieved the lowest overall root mean squared error (RMSE) (1.356). When additional text context was provided, LLM forecasts achieved the lowest MAE for predicting mental sharpness (0.862), fatigue (1.000), and stress (0.414). These multimodal LLMs further outperformed the numeric baselines in terms of RMSE when predicting stress (0.947), although numeric algorithms achieved the best RMSE results for mental sharpness (1.246) and fatigue (1.587). This study offers valuable insights for future applications of LLMs in health-based forecasting. The findings suggest that LLMs, when supplemented with additional text information, can be effective tools for improving health forecasting accuracy.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 2","pages":""},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12330958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144801058","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 method for comparing time series by untangling time-dependent and independent variations in biological processes 通过消除生物过程中与时间相关的独立变化来比较时间序列的方法

ACM transactions on computing for healthcare Pub Date : 2024-07-26 DOI: 10.1145/3681795

A. J. Thottupattu, J. Sivaswamy

引用次数: 0

AI-assisted Diagnosing, Monitoring, and Treatment of Mental Disorders: A Survey 人工智能辅助诊断、监测和治疗精神障碍：一项调查

ACM transactions on computing for healthcare Pub Date : 2024-07-25 DOI: 10.1145/3681794

Faustino Muetunda, Soumaya Sabry, M. Jamil, Sebastião Pais, Gael Dias, João Cordeiro

{"title":"AI-assisted Diagnosing, Monitoring, and Treatment of Mental Disorders: A Survey","authors":"Faustino Muetunda, Soumaya Sabry, M. Jamil, Sebastião Pais, Gael Dias, João Cordeiro","doi":"10.1145/3681794","DOIUrl":"https://doi.org/10.1145/3681794","url":null,"abstract":"Globally, 1 in 7 people has some kind of mental or substance use disorder that affects their thinking, feelings, and behaviour in everyday life. People with mental health disorders can continue their normal lives with proper treatment and support. Mental well-being is vital for physical health. The use of AI in mental health areas has grown exponentially in the last decade. However, mental disorders are still complex to diagnose due to similar and common symptoms for numerous mental illnesses, with a minute difference. Intelligent systems can help us identify mental diseases precisely, which is a critical step in diagnosing. Using these systems efficiently can improve the treatment and rapid recovery of patients. We survey different artificial intelligence systems used in mental healthcare, such as mobile applications, machine learning and deep learning methods, and multimodal systems and draw comparisons from recent developments and related challenges. Also, we discuss types of mental disorders and how these different techniques can support the therapist in diagnosing, monitoring, and treating patients with mental disorders.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"16 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HEalthRecordBERT (HERBERT): leveraging transformers on electronic health records for chronic kidney disease risk stratification HEalthRecordBERT (HERBERT)：利用电子健康记录转换器进行慢性肾病风险分层

ACM transactions on computing for healthcare Pub Date : 2024-07-19 DOI: 10.1145/3665899

Alex Moore, B. Orset, A. Yassaee, Benjamin Irving, Davide Morelli

{"title":"HEalthRecordBERT (HERBERT): leveraging transformers on electronic health records for chronic kidney disease risk stratification","authors":"Alex Moore, B. Orset, A. Yassaee, Benjamin Irving, Davide Morelli","doi":"10.1145/3665899","DOIUrl":"https://doi.org/10.1145/3665899","url":null,"abstract":"Risk stratification is an essential tool in the fight against many diseases, including chronic kidney disease. Recent work has focused on applying techniques from machine learning and leveraging the information contained in a patient’s electronic health record (EHR). Irregular intervals between data entries and the large number of variables tracked in EHR datasets can make them challenging to work with. Many of the difficulties associated with these datasets can be overcome by using large language models, such as bidirectional encoder representations from transformers (BERT). Previous attempts to apply BERT to EHR for risk stratification have shown promise. In this work we propose HERBERT, a novel application of BERT to EHR data. We identify two key areas where BERT models must be modified to adapt them to EHR data, namely: the embedding layer and the pretraining task. We show how changes to these can lead to improved performance, relative to the previous state of the art. We evaluate our model by predicting the transition of chronic kidney disease patients to end stage renal disease. The strong performance of our model justifies our architectural changes and suggests that large language models could play an important role in future renal risk stratification.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"115 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Computation Model to Estimate Interaction Intensity through Non-verbal Behavioral Cues: A Case Study of Intimate Couples under the Impact of Acute Alcohol Consumption 通过非语言行为线索估计互动强度的计算模型：急性酒精中毒影响下亲密伴侣的案例研究

ACM transactions on computing for healthcare Pub Date : 2024-07-19 DOI: 10.1145/3664826

Zhiwei, Z.Y. Yu, Cory, C.C. Crane, Linlin, L.C. Chen, Maria, M.T. Testa, Zhi, Z.Z. Zheng

{"title":"A Computation Model to Estimate Interaction Intensity through Non-verbal Behavioral Cues: A Case Study of Intimate Couples under the Impact of Acute Alcohol Consumption","authors":"Zhiwei, Z.Y. Yu, Cory, C.C. Crane, Linlin, L.C. Chen, Maria, M.T. Testa, Zhi, Z.Z. Zheng","doi":"10.1145/3664826","DOIUrl":"https://doi.org/10.1145/3664826","url":null,"abstract":"This work introduced a novel analysis method to estimate interaction intensity, i.e., the level of positivity/negativity of an interaction, for intimate couples (married and heterosexual) under the impact of alcohol, which has great influences on behavioral health. Non-verbal behaviors are critical in interpersonal interactions. However, whether computer vision-detected non-verbal behaviors can effectively estimate interaction intensity of intimate couples is still unexplored. In this work, we proposed novel measurements and investigated their feasibility to estimate interaction intensities through machine learning regression models. Analyses were conducted based on a conflict-resolution conversation video dataset of intimate couples before and after acute alcohol consumption. Results showed the estimation error was at the lowest in the no-alcohol state but significantly increased if the model trained using no-alcohol data was applied to after-alcohol data, indicating that alcohol altered the interaction data in the feature space. While training a model using rich after-alcohol data is ideal to address the performance decrease, data collection in such a risky state is challenging in real life. Thus, we proposed a new State-Induced Domain Adaptation (SIDA) framework, which allows for improving estimation performance using only a small after-alcohol training dataset, pointing to a future direction of addressing data scarcity issues.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":" 1092","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0