Artificial Intelligence in Medicine最新文献_第4页

HMEA: A hierarchical medical knowledge graph entity alignment model fusing multi-aspect information HMEA：融合多方面信息的层次化医学知识图谱实体对齐模型

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-07-11 DOI: 10.1016/j.artmed.2025.103188

Weiguang Wang , Lijuan Ma , Wei Cai , Haiyan Zhao , Xia Zhang

{"title":"HMEA: A hierarchical medical knowledge graph entity alignment model fusing multi-aspect information","authors":"Weiguang Wang , Lijuan Ma , Wei Cai , Haiyan Zhao , Xia Zhang","doi":"10.1016/j.artmed.2025.103188","DOIUrl":"10.1016/j.artmed.2025.103188","url":null,"abstract":"<div><div>Medical entity alignment is crucial for the integration and reasoning of medical knowledge, aiming to match semantically equivalent entities across different medical knowledge graphs. Unlike entities in general knowledge graphs, medical entities contain rich multi-aspect information, which not only includes structural and attribute information but also additional information such as ontology and descriptions. However, existing entity alignment methods overlook these additional pieces of information and lack exploration into the fusion of multi-aspect information. This leads to less-than-ideal performance in medical entity alignment. To address the aforementioned issues, in this paper, we propose a hierarchical medical knowledge graph entity alignment method, termed HMEA, which integrates multi-aspect information. Firstly, we represent the medical knowledge graph as a hierarchical heterogeneous graph to model the multi-aspect information of medical entities. Secondly, we design different representation learning methods according to the characteristics of multi-aspect information to obtain vector representations of entities in different dimensions. Subsequently, we devise a two-stage multi-aspect knowledge fusion mechanism to dynamically integrate multi-aspect information, enabling mutual complementarity. Finally, we utilize the fused entity vector representations to guide entity alignment. We compare our approach with state-of-the-art baseline models on ten different types of publicly available datasets and further conduct ablation and parameter analyses. Experimental results validate the effectiveness and robustness of the proposed model. In benchmark tests across all datasets, HMEA outperforms the current state-of-the-art methods significantly.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"168 ","pages":"Article 103188"},"PeriodicalIF":6.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia 基于人工智能的生物医学文献挖掘：用于治疗痴呆症的药物再利用的应用

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-07-10 DOI: 10.1016/j.artmed.2025.103218

Aliaksandra Sikirzhytskaya , Ilya Tyagin , S. Scott Sutton , Michael D. Wyatt , Ilya Safro , Michael Shtutman

{"title":"AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia","authors":"Aliaksandra Sikirzhytskaya , Ilya Tyagin , S. Scott Sutton , Michael D. Wyatt , Ilya Safro , Michael Shtutman","doi":"10.1016/j.artmed.2025.103218","DOIUrl":"10.1016/j.artmed.2025.103218","url":null,"abstract":"<div><div>Neurodegenerative diseases like Alzheimer's, Parkinson's, and HIV-associated neurocognitive disorder severely impact patients and healthcare systems. While effective treatments remain limited, researchers are actively developing ways to slow progression and improve patient outcomes, requiring innovative approaches to handle huge volumes of new scientific data. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20,889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. We focused on repurposing drugs for dementia by identifying dementia-associated genes highly ranked in other disease classes. The method was developed for detection of genes that shared across multiple conditions and classified them based on their roles in biological pathways. This led to the selection of six primary drugs for further study.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"168 ","pages":"Article 103218"},"PeriodicalIF":6.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-07-01 DOI: 10.1016/j.artmed.2025.103201

Fransiskus Serfian Jogo

引用次数: 0

Uncovering the genetic basis of glioblastoma heterogeneity through multimodal analysis of whole slide images and RNA sequencing data 通过对整个幻灯片图像和RNA测序数据的多模态分析，揭示胶质母细胞瘤异质性的遗传基础

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-06-26 DOI: 10.1016/j.artmed.2025.103191

Ahmad Berjaoui , Eduardo Hugo Sanchez , Louis Roussel , Elizabeth Cohen-Jonathan Moyal

引用次数: 0

Discovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery 使用不同的top-k亚群列表发现多种抗生素耐药表型

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-06-26 DOI: 10.1016/j.artmed.2025.103200

Antonio Lopez-Martinez-Carrasco , Hugo M. Proença , Jose M. Juarez , Matthijs van Leeuwen , Manuel Campos

{"title":"Discovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery","authors":"Antonio Lopez-Martinez-Carrasco , Hugo M. Proença , Jose M. Juarez , Matthijs van Leeuwen , Manuel Campos","doi":"10.1016/j.artmed.2025.103200","DOIUrl":"10.1016/j.artmed.2025.103200","url":null,"abstract":"<div><div>Antibiotic resistance is one of the major global threats to human health and occurs when antibiotics lose their ability to combat bacterial infections. In this problem, a clinical decision support system could use phenotypes in order to alert clinicians of the emergence of patterns of antibiotic resistance in patients. Patient phenotyping is the task of finding a set of patient characteristics related to a specific medical problem such as the one described in this work. However, a single explanation of a medical phenomenon might be useless in the eyes of a clinical expert and be discarded. The discovery of multiple patient phenotypes for the same medical phenomenon would be useful in such cases. Therefore, in this work, we define the problem of mining diverse top-k phenotypes and propose the EDSLM algorithm, which is based on the Subgroup Discovery technique, the subgroup list model, and the Minimum Description Length principle. Our proposal provides clinicians with a method with which to obtain multiple and diverse phenotypes of a set of patients. We show a real use case of phenotyping in antimicrobial resistance using the well-known MIMIC-III dataset.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103200"},"PeriodicalIF":6.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ContraDTI: Improved drug–target interaction prediction via multi-view contrastive learning 通过多视角对比学习改进药物-靶标相互作用预测

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-06-25 DOI: 10.1016/j.artmed.2025.103195

Zhirui Liao , Lei Xie , Shanfeng Zhu

{"title":"ContraDTI: Improved drug–target interaction prediction via multi-view contrastive learning","authors":"Zhirui Liao , Lei Xie , Shanfeng Zhu","doi":"10.1016/j.artmed.2025.103195","DOIUrl":"10.1016/j.artmed.2025.103195","url":null,"abstract":"<div><div>Drug–target interaction (DTI) identification is one of the crucial issues in the field of drug discovery. Machine learning approaches offer efficient ways to address this issue, reducing expensive and time-consuming laboratory experiments. However, the scarcity of annotated drug data with labels restricts supervised machine learning applications to DTI prediction. Drawing inspiration from recent advances in contrastive learning, we present ContraDTI—a novel framework that adopts multi-view contrastive learning to overcome data limitations in this paper. Our model considers the molecular graph of a drug as the main view and the SMILES string of a drug as the side view, employing two types of loss functions for the contrast of the main view and the cross-view alignment between the main and the side views. Extensive experiments on both single-target and multi-target DTI datasets demonstrate that ContraDTI enhances the classification performance of DTI prediction, particularly when labeled data is scarce. ContraDTI can be a powerful tool for DTI prediction in data-limited scenarios. The code of this paper is available at <span><span>https://github.com/zhiruiliao/ContraDTI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"168 ","pages":"Article 103195"},"PeriodicalIF":6.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RobustEMD: Domain robust matching for cross-domain few-shot medical image segmentation RobustEMD：基于域鲁棒匹配的医学图像分割方法

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-06-24 DOI: 10.1016/j.artmed.2025.103197

Yazhou Zhu , Minxian Li , Qiaolin Ye , Shidong Wang , Tong Xin , Haofeng Zhang

{"title":"RobustEMD: Domain robust matching for cross-domain few-shot medical image segmentation","authors":"Yazhou Zhu , Minxian Li , Qiaolin Ye , Shidong Wang , Tong Xin , Haofeng Zhang","doi":"10.1016/j.artmed.2025.103197","DOIUrl":"10.1016/j.artmed.2025.103197","url":null,"abstract":"<div><div>Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current FSMIS models are all trained and deployed on the same data domain, as is not consistent with the clinical reality that medical imaging data is always across different data domains (e.g. imaging modalities, institutions and equipment sequences). In this paper, we introduce Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) and propose a RobustEMD matching mechanism based on Earth Mover’s Distance (EMD) to enhance cross-domain generalization. Our approach includes three key components: (1) a channel-wise feature decomposition strategy that uniformly divides support and query features into local nodes, (2) a texture structure aware weights generation method that restrains domain-relevant nodes through Sobel-based gradient calculation, and (3) a boundary-aware Hausdorff distance measurement for transportation cost calculation. Extensive experiments across three scenarios (cross-modal, cross-sequence and cross-institution) show that our method significantly outperforms existing approaches. And ablation studies further confirm that each component of our RobustEMD mechanism contributes to the enhanced performance. The experimental outcomes highlight strong generalization capabilities of our model in real-world heterogeneous medical imaging environments. Code is available at <span><span>https://github.com/YazhouZhu19/RobustEMD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103197"},"PeriodicalIF":6.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GraphCF: Drug–target interaction prediction via multi-feature fusion with contrastive graph neural network GraphCF：基于对比图神经网络的多特征融合药物-靶标相互作用预测

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-06-24 DOI: 10.1016/j.artmed.2025.103196

Dianlei Gao, Fei Zhu

{"title":"GraphCF: Drug–target interaction prediction via multi-feature fusion with contrastive graph neural network","authors":"Dianlei Gao, Fei Zhu","doi":"10.1016/j.artmed.2025.103196","DOIUrl":"10.1016/j.artmed.2025.103196","url":null,"abstract":"<div><div>Drug–target interaction (DTI) is paramount in drug discovery and repurposing, which involves screening for effective candidate drugs by targeting specific proteins. Existing methods often focus on one or two representations of drugs or targets, and little has been explored regarding 3D structures. Moreover, how to capture interactions between multi-modal features comprehensively is also a key issue. A multi-modal interaction fusion method called GraphCF is proposed to overcome these limitations. Specifically, GraphCF uses a MixHop aggregator to gather higher-order neighborhood information between nodes in the DTI topological network and incorporate graph contrastive learning to capture more discriminative 2D representations of drugs and targets. Additionally, GraphCF utilizes convolutional neural networks and graph neural networks to extract the sequence and 3D structural features of drugs and targets, respectively. Then, GraphCF employs a cross-attention-based multi-feature fusion module to facilitate information interaction and fusion among multi-modal feature representations. GraphCF is evaluated and compared with some advanced methods on four public datasets, and the results demonstrate the competitive performance of GraphCF in DTI prediction.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103196"},"PeriodicalIF":6.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online continuous learning of users suicidal risk on social media 在线持续学习用户在社交媒体上的自杀风险

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-06-24 DOI: 10.1016/j.artmed.2025.103199

Lei Cao , Ling Feng , Yang Ding , Huijun Zhang , Xin Wang , Kaisheng Zeng , Yi Dai

{"title":"Online continuous learning of users suicidal risk on social media","authors":"Lei Cao , Ling Feng , Yang Ding , Huijun Zhang , Xin Wang , Kaisheng Zeng , Yi Dai","doi":"10.1016/j.artmed.2025.103199","DOIUrl":"10.1016/j.artmed.2025.103199","url":null,"abstract":"<div><div>Suicide is a tragedy for family and society. With social media becoming an integral part of people’s life nowadays, assessing suicidal risk based on one’s social media behavior has drawn increasing research attentions. The majority of the works trained a machine learning model to classify user’s suicidal risk severity level in a batch learning setting on the entire training data. This is not a timely and scalable solution in the context of social media where new data arrives sequentially in a stream form. In this study, we formulate and address the continuous suicidal risk assessment problem through a three-layered joint memory network, consisting of a short-term personal memory and long-term personal and global memories. Unlike existing methods that rely on static classification, our model supports real-time, continuous learning from users’ emotional and behavioral dynamics without the need for full retraining. This allows for personalized and adaptive risk tracking over time. We also present a way to continuously capture users’ personal features and integrate them in suicidal risk assessment. The performance on the constructed dataset containing 95 suicidal and 95 non-suicidal social media users shows that 96% of accuracy can be achieved with the proposed method.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103199"},"PeriodicalIF":6.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero- and few-shot Named Entity Recognition and Text Expansion in medication prescriptions using large language models 基于大语言模型的药物处方零弹和少弹命名实体识别和文本扩展

IF 6.1 2区医学

Artificial Intelligence in Medicine Pub Date : 2025-06-20 DOI: 10.1016/j.artmed.2025.103165

Natthanaphop Isaradech , Andrea Riedel , Wachiranun Sirikul , Markus Kreuzthaler , Stefan Schulz

{"title":"Zero- and few-shot Named Entity Recognition and Text Expansion in medication prescriptions using large language models","authors":"Natthanaphop Isaradech , Andrea Riedel , Wachiranun Sirikul , Markus Kreuzthaler , Stefan Schulz","doi":"10.1016/j.artmed.2025.103165","DOIUrl":"10.1016/j.artmed.2025.103165","url":null,"abstract":"<div><div>Medication prescriptions in electronic health records (EHR) are often in free-text and may include a mix of languages, local brand names, and a wide range of idiosyncratic formats and abbreviations. Large language models (LLMs) have shown a promising ability to generate text in response to input prompts. We use ChatGPT3.5 to automatically structure and expand medication statements in discharge summaries and thus make them easier to interpret for people and machines. Named Entity Recognition (NER) and Text Expansion (EX) are used with different prompt strategies in a zero- and few-shot setting. 100 medication statements were manually annotated and curated. NER performance was measured by using strict and partial matching. For the EX task, two experts interpreted the results by assessing semantic equivalence between original and expanded statements. The model performance was measured by precision, recall, and F1 score. For NER, the best-performing prompt reached an average F1 score of 0.94 in the test set. For EX, the few-shot prompt showed superior performance among other prompts, with an average F1 score of 0.87. Our study demonstrates good performance for NER and EX tasks in free-text medication statements using ChatGPT3.5. Compared to a zero-shot baseline, a few-shot approach prevented the system from hallucinating, which is essential when processing safety-relevant medication data. We tested ChatGPT3.5-tuned prompts on other LLMs, including ChatGPT4o, Gemini 2.0 Flash, MedLM-1.5-Large, and DeepSeekV3. The findings showed most models outperformed ChatGPT3.5 in NER and EX tasks.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103165"},"PeriodicalIF":6.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0