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multimodal data integration in early-stage breast cancer

BREAST 2025年 80卷 103892-103892页

作者： Llinas-Bertran, Arnau Butjosa-Espin, Maria Barberi, Vittoria Seoane, Jose A. Vall dHebron Inst Oncol VHIO Canc Computat Biol Grp Barcelona Spain Vall dHebron Inst Oncol VHIO Breast Canc Grp Barcelona Spain

The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors. The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers. This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.

关键词： data integration Machine learning Deep learning Stratification multimodal data integration Multi-omics

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multimodal data integration via mediation analysis with high-dimensional exposures and mediators

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HUMAN BRAIN MAPPING 2022年第8期43卷 2519-2533页

作者： Zhao, Yi Li, Lexin Indiana Univ Sch Med Dept Biostat & Hlth Data Sci Indianapolis IN 46202 USA Univ Calif Berkeley Dept Biostat & Epidemiol Berkeley CA 94720 USA

Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high-dimensional exposures and high-dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein-structure-memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.

关键词： Alzheimer's disease mediation analysis multimodal data integration neuroimaging principal component analysis

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Granular Linguistic Model Based multimodal data integration for Automated Evaluation of Core Soft Skills 15th

Granular Linguistic Model Based Multimodal Data Integration ...

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15th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Guerrero-Sosa, Jared D. T. Romero, Francisco P. Menendez, Victor H. Serrano-Guerrero, Jesus Olivas, Jose A. Montoro-Montarroso, Andres Univ Castilla La Mancha Dept Informat Syst & Technol Paseo Univ 4 Ciudad Real Spain Autonomous Univ Yucatan Sch Math Merida 13615 Mexico

ISBN: (纸本)9783031486418;9783031486425

This research introduces an innovative hybrid intelligence framework leveraging multimodal data to automate the evaluation of core soft skills, including decision-making, conflict resolution, and creativity. The model applies the principles of the Granular Linguistic Model of Phenomena (GLMP), a sophisticated method that delineates phenomena at varying granularity levels, ensuring a detailed analysis of the exhibited skills. The process involves mining significant behavioural features from diverse data sources, specifically video, audio, and text, employing deep learning algorithms. The extracted features are then subjected to the GLMP, representing the students' behaviour in a structured, interpretable format across multiple granularities. The GLMP application yields an exhaustive set of granular linguistic prompts that encapsulate the complexity of the identified soft skills. This multimodal information feeds into a fuzzy logic-based detector that evaluates the defined soft skills. This integrative approach merges granular linguistic modelling with multimodal data, enabling a comprehensive and accessible understanding of the students' soft skills. The implications of this approach extend beyond the academic sphere, finding utility in broader contexts such as college admissions and job recruitment, where objective skill evaluation is crucial. This research underscores the value of multimodal integration within the GLMP framework, highlighting its critical role in translating raw data into actionable insights. It further illuminates the potential of such methods in enhancing real-world decision-making processes and outcomes.

关键词： multimodal data integration Granular Linguistic Model of Phenomena Soft Skills Evaluation

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Early multimodal data integration for data-Driven Medical Research - A Scoping Review

Early Multimodal Data Integration for Data-Driven Medical Re...

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69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024

作者： Gehrmann, Julia Beyan, Oya Institute for Biomedical Informatics University of Cologne Faculty of Medicine University Hospital Cologne Cologne Germany Department of Data Science and Artificial Intelligence Fraunhofer FIT Sankt Augustin Germany

ISBN: (纸本)9781643685366

Introduction data-driven medical research (DDMR) needs multimodal data (MMD) to sufficiently capture the complexity of clinical cases. Methods for early multimodal data integration (MMDI), i.e. integration of the data before performing a data analysis, vary from basic concatenation to applying Deep Learning, each with distinct characteristics and challenges. Besides early MMDI, there exists late MMDI which performs modality-specific data analyses and then combines the analysis results. Methods We conducted a scoping review, following PRISMA guidelines, to find and analyze 21 reviews on methods for early MMDI between 2019 and 2024. Results Our analysis categorized these methods into four groups and summarized group-specific characteristics that are relevant for choosing the optimal method combination for MMDI pipelines in DDMR projects. Moreover, we found that early MMDI is often performed by executing several methods subsequently in a pipeline. This early MMDI pipeline is usually subject to manual *** Our focus was on structural integration in DDMR. The choice of MMDI method depends on the research setting, complexity, and the researcher team's expertise. Future research could focus on comparing early and late MMDI approaches as well as automating the optimization of MMDI pipelines to integrate vast amounts of real-world medical data effectively, facilitating holistic DDMR. © 2024 The Authors.

关键词： data-Driven Medicine Early integration multimodal data integration multimodality Scoping Review

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multimodal neuroimaging data integration and pathway analysis

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BIOMETRICS 2021年第3期77卷 879-889页

作者： Zhao, Yi Li, Lexin Caffo, Brian S. Indiana Univ Sch Med Dept Biostat Indianapolis IN 46202 USA Univ Calif Berkeley Dept Biostat & Epidemiol Berkeley CA 94720 USA Johns Hopkins Univ Dept Biostat Baltimore MD 21205 USA

With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing.

关键词： brain connectivity analysis linear structural equation model mediation analysis multimodal data integration regularization

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Harnessing Artificial Intelligence in multimodal Omics data integration: Paving the Path for the Next Frontier in Precision Medicine

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Biomedical data Science 1000年第1期7卷 225-250页

作者： Yonghyun Nam Jaesik Kim Sang-Hyuk Jung Jakob Woerner Erica H. Suh Dong-gi Lee Manu Shivakumar Matthew E. Lee Dokyoon Kim 1Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA email: dokyoon.kim@pennmedicine.upenn.edu 2Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania USA 3Institute for Biomedical Informatics Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

关键词： machine learning multiomics data integration multimodal data integration precision medicine single-cell omics longitudinal analysis imaging phenotypes biobank risk assessment

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Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine

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JOURNAL OF MEDICAL INTERNET RESEARCH 2025年 27卷 e59069页

作者： Zhang, Kuo Meng, Xiangbin Yan, Xiangyu Ji, Jiaming Liu, Jingqian Xu, Hua Zhang, Heng Liu, Da Wang, Jingjia Wang, Xuliang Gao, Jun Wang, Yuan-geng-shuo Shao, Chunli Wang, Wenyao Li, Jiarong Zheng, Ming-Qi Yang, Yaodong Tang, Yi-Da Chinese Acad Med Sci & Peking Union Med Coll Fuwai Hosp Natl Ctr Cardiovasc Dis Dept CardiolState Key Lab Cardiovasc Dis Beijing Peoples R China PengCheng Lab Shenzhen Guangdong Peoples R China Tianjin Univ Sch Disaster & Emergency Med Tianjin Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China China Telecom Beijing Peoples R China Hong Kong Univ Sci & Technol Div Emerging Interdisciplinary Areas Hong Kong Peoples R China Hefei Univ Technol Artificial Intelligence Inst Hefei 230009 Anhui Peoples R China Hebei Med Univ Hosp 1 Grad Sch Dept Cardiol Shijiazhuang Hebei Peoples R China Peking Univ Third Hosp Dept Cardiol 49 North Garden Rd Beijing 100191 Peoples R China Peking Univ Third Hosp Inst Vasc Med Minist Educ Key Lab Mol Cardiovasc Sci 49 North Garden Rd Beijing 100191 Peoples R China Univ Reading Henley Business Sch Reading RG6 6UD England

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

关键词： large language models LLMs digital health medical diagnosis treatment multimodal data integration technological fairness artificial intelligence AI natural language processing NLP

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A Hybrid Transformer-ARIMA Model for Forecasting Global Supply Chain Disruptions Using multimodal data

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 535-543页

作者： Wang, Qingzi Jiangsu Maritime Inst JMI Int Econ & Trade Nanjing 210000 Jiangsu Peoples R China

This study presents a robust forecasting model for global supply chain disruptions: port delays, natural disasters, geopolitical events, and pandemics. An integrated solution combining the help of transformer-based models for unstructured textual data preprocessing and ARIMA for structured time series analysis is referred to as a hybrid model. This model combines the insights from both approaches using a feature fusion mechanism. It evaluated the Hybrid Model using accuracy, precision, recall, and finally, F1 score, and it was found to perform much better, generally obtaining an overall accuracy of 94.2% and an overall weighted F1 score of 94.3%. Specifically, class-specific analysis demonstrated high precision in identifying disruptions such as pandemics (95.5%) and natural disasters (94.6%), showing the ability of a model to understand context and time. The proposed approach outperforms classic stand-alone statistical and deep learning models regarding scalability and adaptivity to real-life applications such as risk management and policy making. Future work could include making the weights for each cluster dynamic to optimize weights based on real-time trends and improving accuracy and resilience.

关键词： Supply chain disruptions forecasting models hybrid model transformer architecture ARIMA multimodal data integration

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Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions

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COMPUTER SCIENCE REVIEW 2025年 57卷

作者： Annan, Richard Qingge, Letu North Carolina A&T State Univ Dept Comp Sci Greensboro NC 27411 USA

The COVID-19 pandemic has accelerated the use of AI and ML in healthcare, improving diagnosis, treatment, and resource allocation. This survey examines the AI applications in disease detection, differential diagnosis, and post-COVID complication analysis. Our findings show that 53% of the reviewed studies focus on COVID19 detection, while only 14% address post-COVID complications. This reveals a gap in long-term patient monitoring. Convolutional Neural Networks (CNNs) are the most frequently used models, appearing in 23% of reviewed studies as standalone architectures and even more often in hybrid models. Meanwhile, transformers and multimodal models remain underutilized. Each appears in only 4% of the studies, limiting the integration of diverse data sources, such as imaging, audio, and lab results. Federated learning, a privacy-preserving AI approach, appears in 9% of studies. However, it is still less common than centralized models. This restricts secure and collaborative AI development. Despite these progress, challenges such as data bias, limited model generalization, and ethical concerns persist. Advanced methods including transformer models and knowledge distillation offer potential solutions for improving computational efficiency. To strengthen AI-driven healthcare, this survey highlights three key needs: (1) broader adoption of multimodal AI, (2) development of computationally efficient and interpretable AI models, and (3) increased use of federated learning to support privacy-preserving AI training. By synthesizing insights from various studies, this paper provides a comprehensive evaluation of AI innovations in COVID-19 research and outlines key directions for future advancements in ethical and scalable AI-driven healthcare.

关键词： Artificial intelligence COVID-19 detection Federated learning multimodal data integration Privacy-preserving AI

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Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications

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Medical Image Analysis 2025年

作者： Daan Schouten Giulia Nicoletti Bas Dille Catherine Chia Pierpaolo Vendittelli Megan Schuurmans Geert Litjens Nadieh Khalili Department of Pathology Research Institute for Medical Innovation Radboud University Medical Center Nijmegen The Netherlands Oncode Institute Utrecht The Netherlands Department of Electronics and Telecommunications Polytechnic University of Turin Turin Italy Department of Surgical-Medical Sciences Unversity of Turin Turin Italy Department of Radiology and Nuclear Medicine Erasmus University Medical Centre Rotterdam The Netherlands Department of Dermatology Erasmus University Medical Centre Rotterdam The Netherlands Department of Pathology Erasmus University Medical Centre Rotterdam The Netherlands

Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024. We provide an extensive overview of multimodal AI development across different medical disciplines, examining various architectural approaches, fusion strategies, and common application areas. Our analysis reveals that multimodal AI models consistently outperform their unimodal counterparts, with an average improvement of 6.2 percentage points in AUC. However, several challenges persist, including cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets. We critically assess the technical and practical challenges in developing multimodal AI systems and discuss potential strategies for their clinical implementation, including a brief overview of commercially available multimodal AI models for clinical decision-making. Additionally, we identify key factors driving multimodal AI development and propose recommendations to accelerate the field’s maturation. This review provides researchers and clinicians with a thorough understanding of the current state, challenges, and future directions of multimodal AI in medicine.

关键词： multimodal AI Deep learning multimodal data integration Medical imaging

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