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Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources

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arXiv 2024年

作者： Li, Yiming Viswaroopan, Deepthi He, William Li, Jianfu Zuo, Xu Xu, Hua Tao, Cui McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Electrical & Computer Engineering Pratt School of Engineering Duke University 305 Tower Engineering Building DurhamNC27708 United States Department of Artificial Intelligence and Informatics Mayo Clinic JacksonvilleFL32224 United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States

Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance. Methods In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n=621), Twitter (n=9,133), and Reddit (n=131) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance. Results The ensemble model achieved the highest performance in "vaccine," "shot," and "ae," with strict F1-scores of 0.878, 0.930, and 0.925, respectively, along with a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance. Conclusion In conclusion,

关键词： Vaccines

Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation

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arXiv 2025年

作者： Lyu, Shen-Huan He, Yi-Xiao Wang, Yanyan Qu, Zhihao Tang, Bin Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China

Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been proven to have a stronger representation ability than DT, as it provides a way to create shallower tree structures while still approximating complex decision boundaries. However, its learning efficiency is still insufficient, since the linear projections cannot be transmitted to the child nodes, resulting in a waste of model parameters. In this work, we propose an enhanced ODT method with Feature Concatenation (FC-ODT), which enables in-model feature transformation to transmit the projections along the decision paths. Theoretically, we prove that our method enjoys a faster consistency rate w.r.t. the tree depth, indicating that our method possesses a significant advantage in generalization performance, especially for shallow trees. Experiments show that FC-ODT can outperform the other state-of-the-art decision trees with a limited tree depth. Copyright © 2025, The Authors. All rights reserved.

关键词： Efficiency

FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system

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Heliyon 2024年第22期10卷 e40350页

作者： He, Keke Zhu, Jun Li, Limiao Gou, Fangfang Wu, Jia School of Computer Science and Engineering Changsha University Changsha 410003 China Hunan University of Medicine General Hospital Huaihua 418000 China Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance Hunan University of Medicine Huaihua 418000 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang 550025 China Research Center for Artificial Intelligence Monash University Melbourne Clayton 3800 VIC Australia

Many important information in medical research and clinical diagnosis are obtained from medical images. Among them, digital pathology images can provide detailed tissue structure and cellular information, which has become the gold standard for clinical tumor diagnosis. With the development of neural networks, computer-aided diagnosis presents the identification results of various cell nuclei to doctors, which facilitates the identification of cancerous regions. However, deep learning models require a large amount of annotated data. Pathology images are expensive and difficult to obtain, and insufficient annotation data can easily lead to biased results. In addition, when current models are evaluated on an unknown target domain, there are errors in the predicted boundaries. Based on this, this study proposes a feature alignment-based detail recognition strategy for pathology image segmentation (FASNet). It consists of a preprocessing model and a segmentation network (UNW). The UNW network performs instance normalization and categorical whitening of feature images by inserting semantics-aware normalization and semantics-aware whitening modules into the encoder and decoder, which achieves the compactness of features of the same class and the separation of features of different classes. The FASNet method can identify the feature detail information more efficiently, and thus differentiate between different classes of tissues effectively. The experimental results show that the FASNet method has a Dice Similarity Coefficient (DSC) value of 0.844. It achieves good performance even when faced with test data that does not match the distribution of the training data. Code: https://***/zlf010928/***. © 2024

关键词： Assisted diagnosis Deep learning Digital pathology images Feature alignment Insufficient annotation sets

Machine Learning Development in Solving Critical Medical Problems

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Machine Learning Development in Solving Critical Medical Pro...

International Conference on Inventive Computation Technologies (ICICT)

作者： Shaista Fatima G Sangeetha P Ponmurugan A. N. Arularasan A. Prabhu Chakkaravarthy R Denis A. Chinnasamy Department of Electronics and Communication Engineering MJ college Hyderabad Telangana India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Thiruvallur Tamil Nadu India Department of Research Meenakshi Academy of Higher Education and Research Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Networking and Communications School of Computing College of Engineering and Technology Kattankulathur Tamil Nadu India Department of Computer Science Mount Carmel College Autonomous Bengaluru Karnataka India Department of data science and business systems School of computing SRMIST Kattankulathur campus Tamil Nadu India

As a result of ML, the healthcare industry undergoes substantial innovation and improvement. As a result, data management, clinical operations, drug research, and surgery are all progressing more quickly. The healthcare sector is now required to use this cutting-edge technology because of the Covid-19 pandemic. More importantly, people stand to benefit the most from technology because it may improve their health outcomes by identifying the best treatment alternatives for them. Thanks to ML's enhanced early disease detection capabilities, the frequency of re-admissions to hospitals and clinics can be reduced. In this article, we'll look at the main applications of machine learning in healthcare as well as its exceptional benefits in changing the industry.

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High-efficiency optical frequency mixing in an all-dielectric metasurface enabled by multiple bound states in the continuum

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Physical Review B 2023年第7期107卷 075441-075441页

作者： Tingting Liu Meibao Qin Feng Wu Shuyuan Xiao Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai 200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China School of Physics and Materials Science Nanchang University Nanchang 330031 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou 510665 China Institute for Advanced Study Nanchang University Nanchang 330031 China Jiangxi Key Laboratory for Microscale Interdisciplinary Study Nanchang University Nanchang 330031 China

We present nonlinear optical four-wave mixing in a silicon nanodisk dimer metasurface. Under the oblique incident plane waves, the designed metasurface exhibits a multiresonant feature with simultaneous excitations of three quasi-bound states in the continuum (BIC). Through employing these quasi-BICs with maximizing electric field energy at the input bump wavelengths, significant enhancement of third-order nonlinear processes including third-harmonic generation, degenerate and nondegenerate four-wave mixing are demonstrated, giving rise to ten frequencies in the visible wavelengths. This work may lead to a new frontier of ultracompact optical mixer for applications in optical circuitry, ultrasensitive sensing, and quantum nanophotonics.

关键词： Nanophotonics Metasurfaces Finite-element method

Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning

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arXiv 2024年

作者： Fang, Qingqing Su, Qinliang Lv, Wenxi Xu, Wenchao Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Artificial Intelligence Sun Yat-sen University Guangdong China Pazhou Lab Guangzhou510330 China

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder’s reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Explaining Agent Behavior in Agent-Based Modeling Using Subgroup Discovery

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SSRN

SSRN 2023年

作者： Romero, Elena Carmona del Jesús, Cristóbal José Cordón, Oscar del Jesus, María José Damas, Sergio Chica, Manuel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Computer Science and Artificial Intelligence DECSAI University of Granada Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Jaen Jaen23071 Spain Leicester School of Pharmacy De Montfort University LeicesterLEI 9BH United Kingdom Dept. of Computer Science University of Jaen Jaen23071 Spain Dept. of Software Engineering University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

Agent-based modeling (ABM) is a popular tool for simulating complex systems. In addition to the key performance indicators ABM provides as main outputs of the modeling process, it also generates valuable information about the agents' roadmap, but the large volume of generated data is difficult to analyze and understand. Explainable artificial intelligence (XAI) techniques can improve the transparency and interpretability of these models. Subgroup Discovery (SD) is a useful tool for XAI that can facilitate the analysis of the data with respect to a variable of interest. In this contribution, we propose a methodology for applying SD to ABM for extracting useful knowledge of agents behavior from the model output. We compare the performance of different SD algorithms and show the effectiveness of our methodology through a case study in the marketing area. We analyze the obtained set of rules from the point of view of a marketing expert. The results show that our methodology can provide a deeper understanding of consumer behavior and market dynamics, providing potential areas of opportunity for marketers. © 2023, The Authors. All rights reserved.

关键词： Consumer behavior

Utilizing Dynamic Spectrum Allocation to Optimize Capacity in High-Speed Fiber Optic Communications

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Utilizing Dynamic Spectrum Allocation to Optimize Capacity i...

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Narmadha T Laxman Sahoo Dhananjay Yadav P. Sharmila Parikshit N. Mahalle Yuvraj Parmar Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

Dynamic spectrum allocation can extensively improve the ability of high-speed fiber optic communications, permitting Fiber-to-the-home (FTTH) and different deployments to be optimized for max performance. This approach makes use of a decentralized approach to allocate spectrum resources on demand, taking into account scalability and efficient aid usage. With the aid of the use of a restrained set of hardware and software, dynamic spectrum allocation can quick and correctly update the communications community to meet changing demands. This generation takes gain of the increasingly more sophisticated optical additives available these days to alter the spectrum in real time rather than relying on static allocation algorithms. This capability reduces the want to frequently reassign spectrum allocations, in addition to the amount of time and sources it requires, permitting the communications network to be managed and controlled extra efficiently and efficiently. Moreover, dynamic spectrum allocation improves the overall performance of current fiber communications structure with the aid of better using current optical components, which may be applied retroactively.

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SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

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arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography