IEEE Journal of Biomedical and Health Informatics最新文献

{"title":"Automated Quantification of Parkinsonian Facial Expressions via a Transformer-Based Multi-View Multimodal Fusion Framework.","authors":"Xinyi Dou, Aixian Li, Chencheng Zhang, Lian Gu, Jie Ren, Xiaohua Qian","doi":"10.1109/JBHI.2026.3744245","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3744245","url":null,"abstract":"<p><p>Facial hypomimia, a common symptom of Parkinson's disease (PD), is characterized by reduced facial expressiveness due to muscle rigidity. Its clinical assessment, typically based on the Movement Disorder Society-Unified PD Rating Scale, is challenging due to the limited clinician availability, subjectivity, and time consuming procedures. In this paper, we propose a transformer-based multi-view multimodal fusion framework for efficient and objective video-based automated assessment of PD facial expressions. The framework incorporates facial anatomical priors, jointly models global and local features, and progressively fuses complementary information across modalities. It uses a progressive fusion strategy for cross-modal interaction and compensation, together with a spatial-temporal transformer block for anatomically guided landmark modeling of global motion and local subtle dynamics. Our proposed method achieved an accuracy of 68.28% and an acceptable accuracy of 87.75% on the largest clinical dataset (637 videos from 354 PD patients) with four classes. Moreover, testing on an independent dataset from another hospital provides preliminary evidence of the model's cross-center transferability, and the framework was further evaluated on two additional medical facial assessment tasks of stroke facial palsy and facial function. In summary, the proposed framework provides an efficient and reliable solution for the objective assessment of PD facial expressions, with the potential to improve evaluation consistency and support broader clinical applications.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149193217","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}

{"title":"Anatomical-Persistent Synergy Mamba for Joint Pre-to-Postoperative CT Synthesis and Multi-structure Segmentation in Orbital Decompression Surgery.","authors":"Langtao Zhou, Jieliang Shi, Tianyu Fu, Jiaju Zhang, Yangrui Huang, Yiyang Mao, Bangxun Mao, Long Shao, Te Zheng, Danni Ai, Jingfan Fan, Deqiang Xiao, Hong Song, Jinying Zhang, Wencan Wu, Jian Yang","doi":"10.1109/JBHI.2026.3744018","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3744018","url":null,"abstract":"<p><p>Thyroid Eye Disease (TED) often causes proptosis and optic nerve compression, and orbital decompression surgery is commonly performed to relieve these symptoms. However, postoperative anatomical changes are highly nonlinear and patient-specific due to bone removal and orbital content displacement, which makes reliable preoperative prediction both critical and challenging. Existing methods for pre- to postoperative medical image generation often overlook an important clinical prior that, although surgery induces local structural changes, the overall anatomy of the same patient remains largely consistent, and neglecting this can lead to anatomically implausible deviations. To address this issue, we propose APS-Mamba (Anatomical-Persistent Synergy Mamba), a conditional multi-task framework that takes preoperative CT and surgical information as input and jointly performs postoperative CT synthesis and multi-structure segmentation. The model introduces an anatomical persistence constraint to preserve structural consistency in non-operated regions while allowing realistic local deformations in surgery-related areas. It also incorporates a generation-segmentation synergistic learning strategy. In this design, morphological image synthesis provides predicted variations for segmentation, and segmentation enforces semantic constraints to improve the structural fidelity of generated images. Experiments on a clinical TED dataset demonstrate that APS-Mamba outperforms state-of-the-art methods in both image synthesis (PSNR 28.60 dB, SSIM 91.72%) and segmentation (mean DSC 86.17%). Ablation studies further confirm the effectiveness of the anatomical constraint and synergistic learning strategy in modeling anatomical changes.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149193230","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}

{"title":"Q-LCAN: Anatomy-Constrained Quantum-Agentic Systems for MRI-Derived Structural Surgical Digital Twins in Uterine Adenomyosis.","authors":"Yishan Chen, Sun-Wei Guo, Xishi Liu, Xi Wang","doi":"10.1109/JBHI.2026.3744247","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3744247","url":null,"abstract":"<p><p>Accurate patient-specific structural assessment of adenomyosis is essential for fertility-preserving treatment planning, but pelvic T2-weighted MRI is difficult to parse because the uterus, endometrium, ectopic glands, hemorrhagic foci, and adenomyoma are nested, overlapping, and highly imbalanced. We propose Q-LCAN, an anatomy-constrained Quantum-Agentic System that converts MRI into a structured surgical digital twin. A dense 3D decoder first estimates target-specific probability maps and signed distance fields. Five target agents then exchange visual evidence under predefined anatomical axioms, while quantum-enhanced differentiable action projection (Q-DAP) calibrates restricted expand, shrink, or hold actions before vision-language level-set control. A detached anatomical correction module and an independent audit reduce and report residual violations. On 658 multicenter examinations, Q-LCAN achieved case-level five-target macro-average DSC/ASSD of 0.814/2.23 mm in internal five-fold validation and 0.802/2.33 mm in external testing. Compared with the strongest protocol-aligned baseline, Q-LCAN improved macro DSC by 0.018 and 0.019 and reduced ASSD by 0.29 and 0.31 mm, with gains concentrated in ectopic glands, hemorrhagic foci, adenomyoma burden, lesion-to-endometrium distance, residual lesion ratio, and candidate resection-boundary distance. Q-LCAN provides an auditable quantum-agentic framework for MRI-derived structural twins; its millimeter-scale descriptors are intended as preoperative decision support, not autonomous operative guidance.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149193208","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}

{"title":"Uncertainty-Aware Simultaneous Synthesis and Segmentation Via Evidential Deep Learning.","authors":"Blanca Rodriguez-Gonzalez, Ivan Escobar-Corominas, Borja Rodriguez-Vila, Javier Hernandez-Rubia, Juan Maria Perez-Moreno, Angel Torrado-Carvajal, Alejandro Mazal, Norberto Malpica","doi":"10.1109/JBHI.2026.3744206","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3744206","url":null,"abstract":"<p><p>Daily anatomical changes during radiotherapy demand accurate imaging and segmentation to support adaptive treatment. We propose Simultaneous Evidential Network for Synthesis and SEgmentation (SENSSE), a deep learning framework that jointly performs synthetic CT generation and anatomical segmentation from daily CBCT or MR images, while also estimating voxel-wise uncertainty. The architecture integrates a shared encoder and task-specific decoders, trained with a combined loss that promotes cross-task supervision, allowing segmentation to guide synthesis with anatomical priors and synthesis to complement segmentation with structural fidelity. To improve interpretability, the framework incorporates evidential modeling to estimate both data and model-related uncertainty in a single forward pass, resulting in spatial maps that highlight regions of low confidence. These maps support quality assurance and can inform manual review or adaptive thresholds. Evaluations on head and neck and pelvic datasets showed that SENSSE achieved competitive synthesis performance, improved OAR segmentation accuracy and better calibrated uncertainty estimates compared with deterministic and sampling-based baselines. The multitask design improves anatomical consistency and reduces artifacts, while the uncertainty-aware outputs provide additional information to support clinical decision-making. Overall, the framework offers a robust and efficient solution for adaptive radiotherapy workflows, combining multitask learning with uncertainty quantification to support safer and more informed treatment adaptation.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149193258","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}

{"title":"DeMorph: A Decoupled Deformation Model with Anatomical Guidance for Predicting Longitudinal Brain Atrophy.","authors":"Yanhao Zheng, Xin Ye, Junhang Zhang, Zhengxing Huang, Xiang Tian, Hongjing Mao","doi":"10.1109/JBHI.2026.3737688","DOIUrl":"10.1109/JBHI.2026.3737688","url":null,"abstract":"<p><p>Modeling the longitudinal progression of brain atrophy in Alzheimer's Disease (AD) is critical for early diagnosis and prognosis, but remains challenging for existing generative models. Current methods either fail to preserve anatomical integrity or treat progression as an entangled process, where changes from normal aging are inseparably fused with AD pathology. This fundamentally limits their scientific utility. To address this, we propose DeMorph, a novel deformation-based framework that explicitly disentangles and accurately predicts longitudinal brain MRI changes. Our transformer-based architecture features two key innovations: (1) a Decoupled Feed-Forward Network (D-FFN) for separate aging and pathology deformation pathways; and (2) a Multi-Scale Anatomical Guidance Cross-Attention (MS-AGCA) module, which explicitly injects anatomical priors to ensure spatially precise and biologically plausible disentanglement. Extensive experiments on four datasets (ADNI, OASIS-3, AIBL, and an inhouse dataset) demonstrate that DeMorph achieves stateof- the-art performance in both image similarity and anatomical accuracy. Crucially, we validate the success of our framework through robust bidirectional counterfactual synthesis, demonstrating the ability to controllably induce and remove a quantitatively plausible, AD-specific pathological effect.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148066","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}

{"title":"EndoCADx: A Preference-Aligned Vision-Language Framework for Standardized Descriptive Diagnosis in Upper Gastrointestinal Endoscopy.","authors":"Shan Hu, Yuhan Jiang, Tingshun Xiong, Changhong Xiao, Ruiqing Jiang, Zehua Dong, Yiyun Chen, Bin Mei, Lianlian Wu, Zhongyuan Wang","doi":"10.1109/JBHI.2026.3741523","DOIUrl":"10.1109/JBHI.2026.3741523","url":null,"abstract":"<p><p>The subjective nature of descriptive endoscopic diagnosis and the propensity of large vision-language models (LVLMs) to produce semantic hallucinations pose significant threats to clinical safety and standardized patient management. While computer-aided detection (CADe) systems are widely adopted for lesion localization, the transition toward standardized descriptive diagnosis (CADx) remains hindered by large model size, high inference latency, and hallucination risks. To address these barriers, we propose EndoCADx, a compact, low-latency generative framework tailored for standardized gastric descriptive diagnosis. We formalize a two-stage optimization pipeline comprising domain-specific supervised fine-tuning (SFT) and error-driven Direct Preference Optimization (DPO). By systematically mining failure-mode subsets from the initial SFT baseline, we perform targeted recalibration of the model's latent policy to improve concordance between generated descriptions and expert reference annotations. EndoCADx provides an on-demand response time of 1.46 s per physician-selected frozen keyframe, compatible with the routine \"freeze-and-inspect\" workflow. Extensive evaluation on EndoGastro-500, a multi-center independent cohort from six heterogeneous clinical centers, demonstrates robust performance: the DPO-aligned model achieves 91.6% Lesion Recognition Accuracy (ALR) and 84.7% Feature Description Accuracy (AFD), with the latter reflecting improved attribute-level consistency with expert annotations. A comprehensive Multi-Reader, Multi-Case (MRMC) study involving 12 endoscopists further reveals that EndoCADx acts as a standardizing agent; while conventional CADe alone yields only moderate inter-rater reliability (Kappa = 0.64), EndoCADx elevates diagnostic consistency to almost perfect agreement (Kappa = 0.88), particularly benefiting novice clinicians with a 17.20% accuracy gain. By balancing real-time efficiency with expert-aligned safety, this work offers a practical paradigm for the clinical translation of endoscopic VLMs as a reliable supplementary aid to professional judgment.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149439","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}

{"title":"MUST: Mean-teacher inspired and Uncertainty-aware Semi-supervised Transformer for cell instance segmentation.","authors":"Yuwen Zeng, Adel Hafiane, Helene Laurent","doi":"10.1109/JBHI.2026.3738295","DOIUrl":"10.1109/JBHI.2026.3738295","url":null,"abstract":"<p><p>Cell instance segmentation in high-resolution microscopy images remains challenging when dense annotations are limited across diverse imaging modalities. This paper presents MUST, a Mean-teacher inspired and Uncertainty-aware Semi-supervised Transformer for cell instance segmentation under limited target-domain annotations. MUST uses adaptive supervised warm-up to stabilize the student model before activating unlabeled consistency learning, and adopts a two-stream mini-batch design to jointly optimize labeled and unlabeled samples. Instead of relying on hard pseudo-labels, an EMA teacher provides soft probability maps as consistency targets for strongly perturbed student predictions. To reduce error propagation from uncertain teacher predictions, MUST further applies entropy- and region-aware reliability weighting together with dynamic unsupervised loss scaling. During inference, sliding-window prediction and test-time augmentation are integrated to improve robustness on high-resolution microscopy images. On the NeurIPS22-CellSeg benchmark, following supervised pre-training on external annotated datasets and using only 10% of the labeled CellSeg training images for supervised optimization, MUST achieves an Overall F1 score of 0.8878 on the official tuning set, reaching the third-best level among representative topperforming challenge methods. These results demonstrate that MUST provides an effective solution for multi-modality cell instance segmentation with reduced target-domain annotation requirements.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148506","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}

{"title":"Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification.","authors":"Luisa Gallee, Meinrad Beer, Michael Gotz","doi":"10.1109/JBHI.2026.3741998","DOIUrl":"10.1109/JBHI.2026.3741998","url":null,"abstract":"<p><p>Explainability is an important requirement for machine learning models in high-risk domains such as medicine. We introduce HierViT, a hierarchical Vision Transformer that combines competitive predictive performance with multiple mechanisms to support the interpretation of its predictions. The model first predicts clinically relevant visual appearance attributes, which are then used to infer the final target prediction through a hierarchical reasoning process. To facilitate understanding of the intermediate representations, attribute-specific prototypes are visualized as representative image examples, while attribute-specific attention maps highlight image regions associated with each visual feature. Together, these attribute-level components provide complementary views of the model's reasoning by linking representative image examples and spatial attention to semantically defined visual features. We evaluate HierViT on two medical benchmark datasets: LIDC-IDRI for lung nodule assessment and derm7pt for skin lesion classification. Across both datasets, HierViT achieves competitive predictive performance while providing attribute-level prototypes and attention visualizations that support the interpretation of its predictions.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147713","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}

{"title":"GraphSC: Deep Graph Learning With Spectral Clustering for Flexible Gene Co-Expression Module Detection Across Pan-Cancer.","authors":"Zixiao Lu, Shuting Yang, Yilin Chen, Zhenghao Lin, Xiaofeng Wu, Qianjin Feng, Siwen Xu","doi":"10.1109/JBHI.2026.3738467","DOIUrl":"10.1109/JBHI.2026.3738467","url":null,"abstract":"<p><p>Identifying gene co-expression (GCE) modules from transcriptomic data is critical for understanding cellular functions and disease mechanisms. Traditional methods often rely on rigid assumptions to detect modules with specific topologies (e.g., spherical clusters or fully connected cliques), limiting their ability to capture the complex and heterogeneous co-expression patterns across different cancer types. Graph convolutional networks (GCNs) have been shown to encode topological structures and node content of networks, which provide a promising direction for more flexible gene co-expression modeling. However, directly applying naïve GCNs to detect GCE modules poses several challenges: sensitivity to expression data noise, lack of explicit mechanisms to obtain arbitrary-shaped modules, and lack of biological ground-truth for supervision. In this study, we propose GraphSC, a graph-based semi-supervised framework for flexible GCE module detection across pan-cancers. To reduce noise sensitivity, GraphSC adopts a graph attention-based encoder-decoder GCN for simultaneous gene embedding learning and expression data reconstruction. GraphSC also integrates a graph-theoretic spectral clustering to organize embeddings into flexible modules without shape constraints. Moreover, we design three loss functions to jointly optimize GraphSC: an unsupervised reconstruction loss for denoising, a structure preservation loss to retain local topology, and a phenotype-supervised module eigengene significance loss to ensure biological relevance. Experiments on eight TCGA datasets demonstrate that GraphSC outperforms state-of-the-art methods in both clustering quality and biological significance. Interpretability analyses further show that GraphSC identifies topologically diverse GCE modules enriched with cancer-specific functions, suggesting its potential for biomarker discovery in different cancers.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147150","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}

{"title":"Semantic Clinical Knowledge Augmentation Improves Medical Question Answering Across Commercial and Open Large Language Models.","authors":"Peter L Elkin, Aaron N Elkin, Guresh Mehta, Frank LeHouillier, Jonathan R Nebeker, Nazmus Saquib, Steven H Brown","doi":"10.1109/JBHI.2026.3742187","DOIUrl":"10.1109/JBHI.2026.3742187","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate whether semantic clinical knowledge augmentation through Semantic Clinical Artificial Intelligence (SCAI) improves medical question answering across commercial and open large language models, and to examine how a reasoning distilled model performs on the same benchmark.</p><p><strong>Materials and methods: </strong>We evaluated Google Gemini, Microsoft Copilot, Meta Llama 3 70B, and DeepSeek-R1-Distill-Llama-70B on official text only United States Medical Licensing Exam (USMLE) sample questions from Step 1 (n = 87), Step 2 CK (n = 103), and Step 3 (n = 123), with and without SCAI. SCAI comprised HD-NLP parsing, graph and knowledge graph embeddings, a trained SCAI LLM semantic knowledge reasoner, and a semantic knowledge base; the current implementation contained 33,023,902 triples across 246 relation labels. Responses were scored against the answer key with secondary human verification. All models produced a response to every text only item, so incorrect responses were counted as confabulations.</p><p><strong>Results: </strong>Across 313 items, accuracy increased from 90.7% to 97.1% for Gemini, 82.4% to 92.0% for Llama 3 70B, and 55.3% to 81.8% for DeepSeek-R1-Distill; Copilot changed from 93.6% to 94.2%. Within model gains were statistically significant for Gemini, Llama 3 70B, and DeepSeek-R1-Distill on all three steps, but not for Copilot. The best single model result was Llama 3 70B plus SCAI on Step 3 (121/123, 98.4%). Gemini plus SCAI was the numerically strongest commercial system.</p><p><strong>Conclusions: </strong>SCAI was associated with materially better accuracy and lower confabulation (False Positive Results) across several heterogeneous LLMs, supporting a semantically grounded pipeline in which a dedicated SCAI LLM generates clinical context for a host LLM while suggesting that strong general reasoning performance does not automatically translate into healthcare readiness.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148024","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}