Proceedings of machine learning research最新文献

Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases. 使用真实世界测试用例对时间序列的缺失数据插入方法进行基准测试。

Proceedings of machine learning research Pub Date : 2025-06-01

Adedolapo Aishat Toye, Asuman Celik, Samantha Kleinberg

{"title":"Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases.","authors":"Adedolapo Aishat Toye, Asuman Celik, Samantha Kleinberg","doi":"","DOIUrl":"","url":null,"abstract":"Missing data is pervasive in healthcare. Many imputation methods exist to fill in missing values, yet most were evaluated using randomly deleted values rather than the actual mechanisms they were designed to address. We aimed to determine real-world accuracy for missing data imputation with three missing data mechanisms (missing completely at random, MCAR; missing at random, MAR; and not missing at random, NMAR) for state of the art and commonly used imputation methods. Using two time series data targets (continuous glucose monitoring, Loop dataset; heart rate, All of Us dataset) we simulated missingness by masking values for each mechanism, at a range of missingness percentages (5-30%) and tested 12 imputation methods. We evaluated accuracy with multiple metrics including root mean square error (RMSE) and bias. We found that overall, accuracy was significantly better on MCAR than on MAR and NMAR, despite many methods being developed for those mechanisms. Linear interpolation had the lowest RMSE with all mechanisms and for all demographic groups, with low bias. This study shows that current evaluation practices do not provide an accurate picture of real world performance with realistic patterns of missingness. Future research is needed to develop evaluation practices that better capture real-world accuracy, and methods that better address real-world mechanisms.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"287 ","pages":"480-501"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981808","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

The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis. 药物不依从性对不良结局的影响：来自精神分裂症患者生存分析的证据。

Proceedings of machine learning research Pub Date : 2025-06-01

Shahriar Noroozizadeh, Pim Welle, Jeremy C Weiss, George H Chen

{"title":"The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis.","authors":"Shahriar Noroozizadeh, Pim Welle, Jeremy C Weiss, George H Chen","doi":"","DOIUrl":"","url":null,"abstract":"This study quantifies the association between non-adherence to antipsychotic medications and adverse outcomes in individuals with schizophrenia. We frame the problem using survival analysis, focusing on the time to the earliest of several adverse events (early death, involuntary hospitalization, jail booking). We extend standard causal inference methods (T-learner, S-learner, nearest neighbor matching) to utilize various survival models to estimate individual and average treatment effects, where treatment corresponds to medication non-adherence. Analyses are repeated using different amounts of longitudinal information (3, 6, 9, and 12 months). Using data from Allegheny County in western Pennsylvania, we find strong evidence that non-adherence advances adverse outcomes by approximately 1 to 4 months. Ablation studies confirm that county-provided risk scores adjust for key confounders, as their removal amplifies the estimated effects. Subgroup analyses by medication formulation (injectable vs. oral) and medication type consistently show that non-adherence is associated with earlier adverse events. These findings highlight the clinical importance of adherence in delaying psychiatric crises and show that integrating survival analysis with causal inference tools can yield policy-relevant insights. We caution that although we apply causal inference, we only make associative claims and discuss assumptions needed for causal interpretation.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"287 ","pages":"573-609"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12444782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115155","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

CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports. CaseReportBench：用于临床病例报告密集信息提取的LLM基准数据集。

Proceedings of machine learning research Pub Date : 2025-06-01

Xiao Yu Cindy Zhang, Carlos R Ferreira, Francis Rossignol, Raymond T Ng, Wyeth Wasserman, Jian Zhu

{"title":"CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports.","authors":"Xiao Yu Cindy Zhang, Carlos R Ferreira, Francis Rossignol, Raymond T Ng, Wyeth Wasserman, Jian Zhu","doi":"","DOIUrl":"","url":null,"abstract":"Rare diseases, including Inborn Errors of Metabolism (IEM), pose significant diagnostic challenges. Case reports serve as key but computationally underutilized resources to inform diagnosis. Clinical dense information extraction refers to organizing medical information into structured predefined categories. Large Language Models (LLMs) may enable scalable information extraction from case reports but are rarely evaluated for this task. We introduce CaseReportBench, an expert-annotated dataset for dense information extraction of case reports (focusing on IEMs). Using this dataset, we assess various models and promptings, introducing novel strategies of category-specific prompting and subheading-filtered data integration. Zero-shot chain-of-thought offers little advantage over zero-shot prompting. Category-specific prompting improves alignment to benchmark. Open-source Qwen2.5:7B outperforms GPT-4o for this task. Our clinician evaluations show that LLMs can extract clinically relevant details from case reports, supporting rare disease diagnosis and management. We also highlight areas for improvement, such as LLMs' limitations in recognizing negative findings for differential diagnosis. This work advances LLM-driven clinical NLP, paving the way for scalable medical AI applications.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"287 ","pages":"527-542"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202365","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

Uncertainty Quantification for Conditional Treatment Effect Estimation under Dynamic Treatment Regimes. 动态处理条件下条件处理效果评估的不确定性量化。

Proceedings of machine learning research Pub Date : 2024-12-01

Leon Deng, Hong Xiong, Feng Wu, Sanyam Kapoor, Soumya Ghosh, Zach Shahn, Li-Wei H Lehman

{"title":"Uncertainty Quantification for Conditional Treatment Effect Estimation under Dynamic Treatment Regimes.","authors":"Leon Deng, Hong Xiong, Feng Wu, Sanyam Kapoor, Soumya Ghosh, Zach Shahn, Li-Wei H Lehman","doi":"","DOIUrl":"","url":null,"abstract":"In medical decision-making, clinicians must choose between different time-varying treatment strategies. Counterfactual prediction via g-computation enables comparison of alternative outcome distributions under such treatment strategies. While deep learning can better model high-dimensional data with complex temporal dependencies, incorporating model uncertainty into predicted conditional counterfactual distributions remains challenging. We propose a principled approach to model uncertainty in deep learning implementations of g-computations using approximate Bayesian posterior predictive distributions of counterfactual outcomes via variational dropout and deep ensembles. We evaluate these methods by comparing their counterfactual predictive calibration and performance in decision-making tasks, using two simulated datasets from mechanistic models and a real-world sepsis dataset. Our findings suggest that the proposed uncertainty quantification approach improves both calibration and decision-making performance, particularly in minimizing risks of worst-case adverse clinical outcomes under alternative dynamic treatment regimes. To our knowledge, this is the first work to propose and compare multiple uncertainty quantification methods in machine learning models of g-computation in estimating conditional treatment effects under dynamic treatment regimes.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"259 ","pages":"248-266"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182919","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

An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation. 儿童肥胖风险评估的可互操作机器学习管道。

Proceedings of machine learning research Pub Date : 2024-12-01

Hamed Fayyaz, Mehak Gupta, Alejandra Perez Ramirez, Claudine Jurkovitz, H Timothy Bunnell, Thao-Ly T Phan, Rahmatollah Beheshti

{"title":"An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation.","authors":"Hamed Fayyaz, Mehak Gupta, Alejandra Perez Ramirez, Claudine Jurkovitz, H Timothy Bunnell, Thao-Ly T Phan, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision support tool based on existing ML models currently exists. This study presents a novel end-to-end pipeline specifically designed for pediatric obesity prediction, which supports the entire process of data extraction, inference, and communication via an API or a user interface. While focusing only on routinely recorded data in pediatric electronic health records (EHRs), our pipeline uses a diverse expert-curated list of medical concepts to predict the 1-3 years risk of developing obesity. Furthermore, by using the Fast Healthcare Interoperability Resources (FHIR) standard in our design procedure, we specifically target facilitating low-effort integration of our pipeline with different EHR systems. In our experiments, we report the effectiveness of the predictive model as well as its alignment with the feedback from various stakeholders, including ML scientists, providers, health IT personnel, health administration representatives, and patient group representatives.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"259 ","pages":"308-324"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11884402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574461","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 Pure Transformer Pretraining Framework on Text-attributed Graphs. 文本属性图的纯变压器预训练框架。

Proceedings of machine learning research Pub Date : 2024-11-01

Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu

{"title":"A Pure Transformer Pretraining Framework on Text-attributed Graphs.","authors":"Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu","doi":"","DOIUrl":"","url":null,"abstract":"Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP. However, progress in the graph domain remains limited due to fundamental challenges represented by feature heterogeneity and structural heterogeneity. Recent efforts have been made to address feature heterogeneity via Large Language Models (LLMs) on text-attributed graphs (TAGs) by generating fixed-length text representations as node features. These high-quality features reduce the previously critical role of graph structure, resulting in a modest performance gap between Graph Neural Networks (GNNs) and structure-agnostic Multi-Layer Perceptrons (MLPs). Motivated by this, we introduce a feature-centric pretraining perspective by treating graph structure as a prior and leveraging the rich, unified feature space to learn refined interaction patterns that generalizes across graphs. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walk and employs masked feature reconstruction to capture pairwise proximity in the LLM-unified feature space using a standard Transformer. By utilizing unified text representations rather than varying structures, GSPT alleviates structural heterogeneity and achieves significantly better transferability among graphs within the same domain. Our approach can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets. The source code is publicly available at https://github.com/SongYYYY/GSPT.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"269 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031307","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

MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions. MedGraphNet：利用多关系图神经网络和文本知识进行生物医学预测。

Proceedings of machine learning research Pub Date : 2024-09-01

Oladimeji Macaulay, Michael Servilla, David Arredondo, Kushal Virupakshappa, Yue Hu, Luis Tafoya, Yanfu Zhang, Avinash Sahu

{"title":"MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions.","authors":"Oladimeji Macaulay, Michael Servilla, David Arredondo, Kushal Virupakshappa, Yue Hu, Luis Tafoya, Yanfu Zhang, Avinash Sahu","doi":"","DOIUrl":"","url":null,"abstract":"Genetic, molecular, and environmental factors influence diseases through complex interactions with genes, phenotypes, and drugs. Current methods often fail to integrate diverse multi-relational biological data meaningfully, limiting the discovery of novel risk genes and drugs. To address this, we present MedGraphNet, a multi-relational Graph Neural Network (GNN) model designed to infer relationships among drugs, genes, diseases, and phenotypes. MedGraphNet initializes nodes using informative embeddings from existing text knowledge, allowing for robust integration of various data types and improved generalizability. Our results demonstrate that MedGraphNet matches and often outperforms traditional single-relation approaches, particularly in scenarios with isolated or sparsely connected nodes. The model shows generalizability to external datasets, achieving high accuracy in identifying disease-gene associations and drug-phenotype relationships. Notably, MedGraphNet accurately inferred drug side effects without direct training on such data. Using Alzheimer's disease as a case study, MedGraphNet successfully identified relevant phenotypes, genes, and drugs, corroborated by existing literature. These findings demonstrate the potential of integrating multi-relational data with text knowledge to enhance biomedical predictions and drug repurposing for diseases. MedGraphNet code is available at https://github.com/vinash85/MedGraphNet.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"261 ","pages":"162-182"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12424194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066688","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

Multimodal Sleep Apnea Detection with Missing or Noisy Modalities. 多模态睡眠呼吸暂停检测缺失或噪声模态。

Proceedings of machine learning research Pub Date : 2024-08-01

Hamed Fayyaz, Niharika S D'Souza, Rahmatollah Beheshti

{"title":"Multimodal Sleep Apnea Detection with Missing or Noisy Modalities.","authors":"Hamed Fayyaz, Niharika S D'Souza, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection. Conventional machine learning methods assume that each sleep study is associated with a fixed set of observed modalities and that all modalities are available for each sample. However, noisy and missing modalities are a common issue in real-world clinical settings. In this study, we propose a comprehensive pipeline aiming to compensate for the missing or noisy modalities when performing sleep apnea detection. Unlike other existing studies, our proposed model works with any combination of available modalities. Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0.9) even in the presence of high levels of noise or missingness. This is especially relevant in settings where the level of noise and missingness is high (such as pediatric or outside-of-clinic scenarios). Our code is publicly available at https://github.com/healthylaife/apnea-missing-modality.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"252 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598009","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

G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes. G-Transformer：动态和时变治疗机制下的反事实结果预测。

Proceedings of machine learning research Pub Date : 2024-08-01

Hong Xiong, Feng Wu, Leon Deng, Megan Su, Zach Shahn, Li-Wei H Lehman

{"title":"G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes.","authors":"Hong Xiong, Feng Wu, Leon Deng, Megan Su, Zach Shahn, Li-Wei H Lehman","doi":"","DOIUrl":"","url":null,"abstract":"In the context of medical decision making, counterfactual prediction enables clinicians to predict treatment outcomes of interest under alternative courses of therapeutic actions given observed patient history. In this work, we present G-Transformer for counterfactual outcome prediction under dynamic and time-varying treatment strategies. Our approach leverages a Transformer architecture to capture complex, long-range dependencies in time-varying covariates while enabling g-computation, a causal inference method for estimating the effects of dynamic treatment regimes. Specifically, we use a Transformer-based encoder architecture to estimate the conditional distribution of relevant covariates given covariate and treatment history at each time point, then produces Monte Carlo estimates of counterfactual outcomes by simulating forward patient trajectories under treatment strategies of interest. We evaluate G-Transformer extensively using two simulated longitudinal datasets from mechanistic models, and a real-world sepsis ICU dataset from MIMIC-IV. G-Transformer outperforms both classical and state-of-the-art counterfactual prediction models in these settings. To the best of our knowledge, this is the first Transformer-based architecture that supports g-computation for counterfactual outcome prediction under dynamic and time-varying treatment strategies.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"252 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12113242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164074","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

Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models. 大型语言模型随机对照试验数值结果的自动提取。

Proceedings of machine learning research Pub Date : 2024-08-01

Hye Sun Yun, David Pogrebitskiy, Iain J Marshall, Byron C Wallace

{"title":"Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models.","authors":"Hye Sun Yun, David Pogrebitskiy, Iain J Marshall, Byron C Wallace","doi":"","DOIUrl":"","url":null,"abstract":"Meta-analyses statistically aggregate the findings of different randomized controlled trials (RCTs) to assess treatment effectiveness. Because this yields robust estimates of treatment effectiveness, results from meta-analyses are considered the strongest form of evidence. However, rigorous evidence syntheses are time-consuming and labor-intensive, requiring manual extraction of data from individual trials to be synthesized. Ideally, language technologies would permit fully automatic meta-analysis, on demand. This requires accurately extracting numerical results from individual trials, which has been beyond the capabilities of natural language processing (NLP) models to date. In this work, we evaluate whether modern large language models (LLMs) can reliably perform this task. We annotate (and release) a modest but granular evaluation dataset of clinical trial reports with numerical findings attached to interventions, comparators, and outcomes. Using this dataset, we evaluate the performance of seven LLMs applied zero-shot for the task of conditionally extracting numerical findings from trial reports. We find that massive LLMs that can accommodate lengthy inputs are tantalizingly close to realizing fully automatic meta-analysis, especially for dichotomous (binary) outcomes (e.g., mortality). However, LLMs-including ones trained on biomedical texts-perform poorly when the outcome measures are complex and tallying the results requires inference. This work charts a path toward fully automatic meta-analysis of RCTs via LLMs, while also highlighting the limitations of existing models for this aim.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"252 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115185","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