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Development of Medical Imaging data Standardization for Imaging-Based Observational Research: OMOP Common data Model Extension

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年第2期37卷 899-908页

作者： Park, Woo Yeon Jeon, Kyulee Schmidt, Teri Sippel Kondylakis, Haridimos Alkasab, Tarik Dewey, Blake E. You, Seng Chan Nagy, Paul Johns Hopkins Univ Biomed Informat & Data Sci 855 N Wolfe StRangos 616 Baltimore MD 21205 USA Yonsei Univ Coll Med Dept Biomed Syst Informat Seoul South Korea Yonsei Univ Inst Innovat Digital Healthcare Seoul South Korea Fdn Res & Technol Hellas FORTH Inst Comp Sci Iraklion Greece Massachusetts Gen Hosp Dept Radiol Boston MA USA Johns Hopkins Univ Dept Neurol Baltimore MD USA

The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.

关键词： data collection [MeSH] data standardization Observational research data integration multimodal data analysis

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An exploratory multimodal study of the roles of teacher-student interaction and emotion in academic performance in online classrooms

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EDUCATION AND INFORMATION TECHNOLOGIES 2025年 1-21页

作者： Xiao, Jun Chen, Mo Yang, Yule Liu, Mengting Shanghai Open Univ Shanghai Engn Res Ctr Open Distance Educ Shanghai Peoples R China Shanghai Normal Univ Sch Educ Shanghai Peoples R China

Teacher-student interaction is critical for providing learners with support and maintaining effective communication during the teaching process. The online teacher-student interaction mode, has posed challenges to the effectiveness of online classes. However, how to conduct effective interaction in online classes to improve learning outcomes remains unclear. This study used multimodal data analysis of platform behavior and emotional data to investigate the impacts of teacher-student interaction and emotions on students' academic performance in an online classroom setting. Findings indicate that adult learners' interactive behaviors, particularly the number of podium appearances and the number of rewards received, positively affect their quiz performance in real time. Additionally, negative emotion frequency collected within a time window shorter than the entire online class was positively correlated with quiz performance. The preliminary results of this study inspire and guide teachers in understanding classroom dynamics and optimizing the teaching process effectively and promptly.

关键词： multimodal data analysis Teacher-student interaction Online teaching Student Academic performance Emotion recognition

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Artificial Intelligence Assisted Student States monitoring based on Enhancing Cognitive and Emotional Feedback Mechanism using Collaborative Learning Environments 3

Artificial Intelligence Assisted Student States monitoring b...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Tantry, Renuka Shenoy, Sharada U. Acharya, Shreenath Prathibha, K. N. St Joseph Engn Coll Dept Artificial Intelligence & Machine Learning Mangaluru India NMAMIT Dept Artificial Intelligence & Machine Learning Nitte India St Joseph Engn Coll Dept Math Mangaluru India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The survey elaborates the specified exploration of rising generation which enhances the pupil's cooperative problem-solving in computer network troubleshooting by analyzing the present literatures and also add-ons to the discussion about improving learning studies through pedagogical origination and technological integration through synthesizing literature evaluation, methodological development, and empirical evaluation. The effects offer clear cut explanation on how AI-driven structures for remarks can transfigure coaching methods and encourage the continued growth of educational strategies. A preliminary exploration is carried out to scrutinize multimodal data, which includesvarious methods to identify students' cognitive and affective conditions, methodically distinct educational results with and without feedback systems, and crafting novel techniques for in-the-moment analysis and adaptation of instructional strategies. In the pilot project, a new approach is introduced called Feedback and Emotion based Student State analysis (FESSA), in which it evaluates the students work in small groups to solve problems cooperatively while their eye movements and emotional reactions are monitored. To understand the emotional and cognitive processes involved in learning, this research based data is evaluated. To examine the effect of feedback structures on learning results and scholar engagement, a comparative analysis is performed. The cause of this assessment is to offer steerage for evidence-based coaching practices in efficient learning *** the end, creative approaches are generatedafter identifying the research gap through literature for the analysis and real-time modification of instructional strategies, emphasizing prompt modifications to student reactions and changing learning necessities. The results of this study are projected to contribute significantly to education on the effectiveness of creative learning strategies and response systems. The mission g

关键词： Pilot Study Problem-Solving Network Troubleshooting Problem-Solving multimodal data analysis

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Learning multimodal entity representations and their ensembles, with applications in a data-driven advisory framework for video game players

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INFORMATION SCIENCES 2022年 617卷 193-210页

作者： Janusz, Andrzej Kaluza, Daniel Matraszek, Maciej Grad, Lukasz Swiechowski, Maciej Slezak, Dominik Univ Warsaw Inst Informat Warsaw Poland QED Software Warsaw Poland

We investigate the impact of combining multiple representations derived from heteroge-neous data sources on the performance of machine learning (ML) models. In particular, we experimentally compare the approach in which independent models are trained on data representations from different sources with the one in which a single model is trained on joined data representations. As a case study, we discuss various entity representation learning methods and their applications in our data-driven advisory framework for video game players, called SENSEI. We show how to use the discussed methods to learn represen-tations of cards and decks for two popular collectible card games (CCGs), namely Clash Royale (CR) and Hearthstone: Heroes of Warcraft (HS). Then, we follow our approach to create ML models which constitute the back-end for several out of SENSEI's end-user func-tionalities. When learning representations, we consider techniques inspired by the NLP domain, as they allow us to create embeddings which capture various aspects of similarity between entities. We put them together with representations composed of manually engi-neered features and standard bags-of-cards. On top of that, we propose a new end2end deep learning architecture with an attention mechanism aimed at reflecting meaningful inter-entity interactions.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Video game analytics Representation learning multimodal data analysis Ensembles in machine learning Attention in neural networks

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Spectral Discovery of Jointly Smooth Features for multimodal data*

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SIAM JOURNAL ON MATHEMATICS OF data SCIENCE 2022年第1期4卷 410-430页

作者： Dietrich, Felix Yair, Or Mulayoff, Rotem Talmon, Ronen Kevrekidis, Ioannis G. Tech Univ Munich Dept Informat D-80333 Munich Germany Technion Israel Inst Technol Viterbi Fac Elect Engn IL-3200003 Haifa Israel Johns Hopkins Univ Dept Chem & Biomol Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA JHMI Baltimore MD 21218 USA

In this paper, we propose a spectral method for deriving functions that are jointly smooth on multiple observed manifolds. This allows us to register measurements of the same phenomenon by heteroge-neous sensors and to reject sensor-specific noise. Our method is unsupervised and primarily consists of two steps. First, using kernels, we obtain a subspace spanning smooth functions on each separate manifold. Then, we apply a spectral method to the obtained subspaces and discover functions that are jointly smooth on all manifolds. We show analytically that our method is guaranteed to provide a set of orthogonal functions that are as jointly smooth as possible, ordered by increasing Dirichlet energy from the smoothest to the least smooth. In addition, we show that the extracted functions can be efficiently extended to unseen data using the Nystro center dot m method. We demonstrate the proposed method on both simulated and real measured data and compare the results to nonlinear, kernel-based variants of the seminal canonical correlation analysis. Particularly, we show superior results for sleep stage identification. In addition, we show how the proposed method can be leveraged for finding minimal realizations of parameter spaces of nonlinear dynamical systems.

关键词： manifold learning SVD CCA kernel methods data fusion multimodal data analysis

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A hybrid filtering for micro-video hashtag recommendation using graph-based deep neural network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Bansal, Shubhi Gowda, Kushaan Rehman, Mohammad Zia Ur Raghaw, Chandravardhan Singh Kumar, Nagendra Indian Inst Technol Dept Comp Sci & Engn Indore India Columbia Univ Dept Comp Sci New York NY USA

Due to the growing volume of user-generated content, hashtags are employed as topic indicators to manage content efficiently on social media platforms. However, finding these vital topics is challenging in micro- videos since they contain substantial information in a short duration. Existing methods that recommend hashtags for micro-videos primarily focus on content and personalization while disregarding user's modality- specific tagging preferences. Moreover, the cold-start user issue prevails in hashtag recommendation systems. Considering the above, we propose a hybrid filtering-based MIcro-video haSHtag recommendatiON (MISHON) system to recommend hashtags for micro-videos. We construct a heterogeneous graph to model user's modality-specific tagging patterns by establishing links with constituent modalities of previous micro-videos, further encompassing user-to-user and modality-to-modality interactions. We then refine modality-specific and user representations using message-passing strategy to recommend pertinent hashtags for micro-videos. The empirical results on three real-world datasets demonstrate that MISHON attains a comparative enhancement of 3.6%, 2.8%, and 6.5% concerning the F1-score, respectively. To address cold-start problem, we propose a content and social influence-based technique to recommend hashtags that are not only relevant to content but also popular, thereby empowering cold-start users to broaden their network and content visibility. The proposed solution shows a relative improvement of 15.8% in the F1-score over its content-only counterpart.

关键词： Hashtag recommendation Micro-videos Graph neural network multimodal data analysis

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Surface Texture-Audio Correlation for multimodal Recognition Enhancement

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Procedia Computer Science 2025年 260卷 360-372页

作者： Shyam Maheshwari Hemant Makwana Institute of Engg. and Tech. Devi Ahilya University Khandwa Road Indore Madhya Pradesh 452001 India

In this research, we study the correlation between surface textures and audio signals produced during the movement using advanced metrics such as Structural Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR) and Histogram of Oriented Gradients (HOG). Results show that rough textures achieve higher SSIM values up to 0.9914, and result in greater preserved structure than the smoother textures. Rough textures reached a PSNR at 29.85 dB, indicating less noise and higher fidelity. In addition, we found the HOG similarity to be as high as 0.9517 for rough textures exhibiting consistent gradient patterns. These results show that rough textures have an advantage when stability of structure and signal quality is desired, contradicting the commonplace preference of smoother textures in multi-modal recognition systems. The results of the study highlight the robustness of integrating visual and auditory modalities for improved recognition accuracy, and have applications in texture generation, biometric authentication, multimedia processing and virtual reality. Further work will be directed towards real time system integration as well as analysis of the broader texture range.

关键词： Human Computer Interaction Pattern Recognition Image-Audio Correlation Similarity Metrics multimodal data analysis

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Alzheimer's disease progression detection model based on an early fusion of cost-effective multimodal data

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 115卷 680-699页

作者： El-Sappagh, Shaker Saleh, Hager Sahal, Radhya Abuhmed, Tamer Islam, S. M. Riazul Ali, Farman Amer, Eslam Univ Santiago de Compostela Ctr Singular Invest Tecnol Intelixentes CiTIUS Santiago De Compostela 15782 Spain Benha Univ Fac Comp & Artificial Intelligence Informat Syst Dept Banha 13518 Egypt South Valley Univ Fac Comp & Artificial Intelligence Hurghada Egypt Hodeidah Univ Comp Sci & Engn Coll Al Hudaydah Yemen Sungkyunkwan Univ Coll Comp Dept Comp Sci & Engn Seoul South Korea Sejong Univ Dept Comp Sci & Engn Seoul South Korea Sejong Univ Dept Software Seoul South Korea VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic Misr Int Univ Fac Comp Sci Cairo Egypt

Alzheimer's disease (AD) is a severe neurodegenerative disease. The identification of patients at high risk of conversion from mild cognitive impairment to AD via earlier close monitoring, targeted investigations, and appropriate management is crucial. Recently, several machine learning (ML) algorithms have been used for AD progression detection. Most of these studies only utilized neuroimaging data from baseline visits. However, AD is a complex chronic disease, and usually, a medical expert will analyze the patient's whole history when making a progression diagnosis. Furthermore, neuroimaging data are always either limited or not available, especially in developing countries, due to their cost. In this paper, we compare the performance of five widely used ML algorithms, namely, the support vector machine, random forest, k-nearest neighbor, logistic regression, and decision tree to predict AD progression with a prediction horizon of 2.5 years. We use 1029 subjects from the Alzheimer's disease neuroimaging initiative (ADNI) database. In contrast to previous literature, our models are optimized using a collection of cost-effective time-series features including patient's comorbidities, cognitive scores, medication history, and demographics. Medication and comorbidity text data are semantically prepared. Drug terms are collected and cleaned before encoding using the therapeutic chemical classification (ATC) ontology, and then semantically aggregated to the appropriate level of granularity using ATC to ensure a less sparse dataset. Our experiments assert that the early fusion of comorbidity and medication features with other features reveals significant predictive power with all models. The random forest model achieves the most accurate performance compared to other models. This study is the first of its kind to investigate the role of such multimodal time-series data on AD prediction. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Alzheimer disease Machine learning multimodal data analysis Disease progression detection

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HAN, image captioning, and forensics ensemble multimodal fake news detection

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INFORMATION SCIENCES 2021年 567卷 23-41页

作者： Meel, Priyanka Vishwakarma, Dinesh Kumar Delhi Technol Univ Dept Informat Technol Biometr Res Lab New Delhi 110042 India

Nowadays, news publication, propagation, and consumption have been diverted to online social media networks and web portals, which has given rise to falsified and fabricated news articles containing both textual and visual information formats. Most of the research to date is centered on textual fake news detection using machine learning approaches, where multimedia data forgery is hardly addressed. Hence, a multimodal fake news detection framework is proposed, which unitedly exploits hidden pattern extraction capabilities from text using Hierarchical Attention Network (HAN) and visual image features using image captioning and forensic analysis. We specifically focused on four different techniques of multimodal data analysis, such as HAN deep model for text, generating image caption and headline matching with news text (CHM), Noise Variance Inconsistency (NVI), and Error Level analysis (ELA). All these algorithms have been tested, first independently and then collectively using the max voting Ensemble method on three different datasets. The experimental results and comparisons with contemporary techniques put forward the fact that the proposed method outperforms state-of-the-art with 95.90% highest accuracy on the Fake News Samples dataset. The achieved results also prove that the combined model beats individual methods' capabilities in classifying fake news accurately. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Fake news detection Hierarchical attention network Image captioning Ensemble multimodal data analysis

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End-to-end multi-perspective multimodal posts relevance score reasoning prediction

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INFORMATION SCIENCES 2024年 675卷

作者： Guo, Xiaoxu Cao, Han Liang, Siyan Shaanxi Normal Univ Sch Comp Sci Xian 710119 Shaanxi Peoples R China Beijing Econ Management Vocat Coll Sch Artificial Intelligence Inst Beijing 100018 Peoples R China Chinese Acad Sci State Key Lab Informat Secur Inst Informat Engn Beijing 100049 Peoples R China

Driven by the surge in students sharing insights on online platforms, evaluating discussion forum posts has emerged as a significant research focus. This assessment is crucial for both students and educators, as it enables teachers to efficiently gauge student comprehension and helps students tailor their learning approaches to suit their individual needs, thereby fostering personalized growth. However, existing research on post-quality assessment faces challenges: 1) They overlook multimodal information interaction between post and topic, leading to inaccurate evaluations. 2) They are usually classification tasks that fail to provide feedback on the minor and relative differences between posts on the same topic. 3) They are usually evaluated using a single perspective, which is insufficient for capturing the complexity of the relationship between post and topic. Based on the above challenges, we propose a new assessment task, multimodal Topic-Post Relevance Score Prediction (MTRSP), which analyzes whether a student's post comprehensively answers the question of the discussion topic by combining text and images to predict topic-post relevance scores, i.e., the degree of correctness of the answer. We develop an end-to-end Multi-perspective Topic-Post Relevance Score Reasoning (MTRSR) Method to solve the MTRSP Task, which leverages images and text from both the post and topic to infer topic-post relevance scores based on semantic similarity and logical coherence. Specifically, the topic-post content relevance reasoning module uses multimodal fusion to learn the semantic similarity of posts and topics. The logical coherence inference module examines the logical connections between posts and topics. Finally, we use three newly collected multimodal topic- post datasets and the public dataset Lazada-Home as an evaluation benchmark for the MTRSP task. Experimental results show that our MTRSR method can bring up to 9.02% in the NDCG@3 (Normalized Discounted Cumulative Gain)

关键词： multimodal data analysis Forum posts Post evaluation

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