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LLM-based kidney disease diagnostic framework for Pathologists 46

LLM-based kidney disease diagnostic framework for Pathologis...

引用

46th Annual international conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Syeda, Masooma Zehra Bukhari, Syed Usama Khalid Hussain, Maqbool Khan, Wajahat Ali Shah, Syed Sajid Hussain University of Derby College of Science and Engineering Derby United Kingdom Idrak Ai Ltd Idrak Artificial Intelligence Limited London United Kingdom Northern Border University Faculty of Medicine Arar Saudi Arabia

ISBN: (纸本)9798350371499

Large language models revolutionize the recent paradigm in the medical field and its contributing to various applications, diversified from clinical decision support to information extraction and summarization. The substantial linguistic understanding and contextual awareness allow language models to process and evaluate decision tasks. Concurrently, it addresses the challenges encountered by pathologists in disease diagnosis by adeptly retrieving precise and accurate facts from an external knowledge base. In this paper, we propose a framework which incorporates advanced retrieval augmented generation with prompt engineering techniques, contain prompting levels and structured prompts, which enables the model to extract refine, and customize responses. The model has been equipped with a large corpus of several kidney diseases clinical data which is collected from the vast information sources of kidney diagnostic books. The utilization of varied prompt techniques, exemplified by standard prompts like few-shots and the Reasoning Act (ReAct), manifests notable improvements in disease diagnosis responses. Structured prompts are designed to provide pathologists with specific instructions for formulating questions that effectively enhance the performance of the model. In the evaluation of prompt performance, three key metrics are employed answer relevance, faithfulness, and context relevance. Notably, in the context relevance metric, an optimal performance score of 1.0 was attained indicating perfect alignment with the conversational context. © 2024 IEEE.

关键词： decision support information retrieval kidney diseases large language models natural language processing retrieval augmented generation

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Simple Contrastive Learning with knowledge Graphs for Story Generation

Simple Contrastive Learning with Knowledge Graphs for Story ...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhu, Yu Pan, Rong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Story generation stands as a crucial, yet formidable task, necessitating a profound grasp of subtle, often unspoken knowledge, along with context-specific cues to craft compelling narratives. The core challenges involve effectively harnessing this implicit knowledge and augmenting narrative diversity. Addressing these hurdles, this paper introduces SimCoS - an acronym for Simple Contrastive learning with knowledge graphs for Story generation. SimCoS significantly bolsters language models' performance in story generation by sharpening their ability to perceive and discern input nuances, aided by a specialized story knowledge graph. Moreover, SimCoS debuts a novel methodology for evaluating 'negative instances' using subgraphs, which enables it to calculate logical distances between inputs, thereby enhancing differentiation among diverse stories. Through both automatic and manual assessments, SimCoS has been shown to outperform leading counterparts in two story generation tasks, marking improvements of 13.4% and 4.5%, respectively. This advancement not only yields narratives of superior quality and logical coherence but also elevates their diversity. © 2025 IEEE.

关键词： contrastive learning knowledge graph story generation

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TCM Automatic Diagnosis System Based on knowledge Graph and BERT 4

TCM Automatic Diagnosis System Based on Knowledge Graph and ...

引用

4th international conference on Artificial Intelligence and Computer engineering, ICAICE 2023

作者： Liu, Yuxin Yu, Jianhui Jia, Wenyang Liu, Yuliang Optoelectronic System Laboratory Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Beijing Boaitang Traditional Chinese Medicine Hospital Beijing China

ISBN: (纸本)9798400708831

Artificial intelligence technology has provided significant benefits to Traditional Chinese Medicine (TCM) diagnosis. In this paper, we build a TCM automatic system utilizing knowledge graphs and natural language processing. We first train a standard word alignment model by fine-tuning the Bidirectional Encoder Representations from Transformers (BERT) based model to help nonstandard input align to standard text. Then we propose an algorithm for calculating the recommended score for each prescription in the knowledge graph, thereby obtaining the recommended results for a given set of patient symptoms. To evaluate the effectiveness of our system, we conducted experiments using our TCM diagnosis dataset. The results demonstrate that our system has the potential to be a traditional Chinese medicine AI assistant with low computing resource consumption and high accuracy. © 2023 Owner/Author.

关键词： knowledge graph

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Deep Hashing Retrieval of Dynamic Corpora 8

Deep Hashing Retrieval of Dynamic Corpora

引用

8th international conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Zou, Ao Hao, Wenning Shao, Tianhao Zhang, Ke Shen, Qiuhui Army Engineering University of Pla College of Command & Control Engineering Nanjing China

ISBN: (纸本)9798350302455

knowledge-intensive language applications, like open-domain question answering, call for NLP systems to retrieve related evidence in a document collection. A efficient and scalable retrieval system is an ideal component in these applications. Up-to-data research developed sequence-to-sequence framework on this task, eschewing any format of term-frequency or query-document similarity calculation. This line of work greatly improved the efficient of retrieval but with the sacrifice of scalablity. This work proposes a new framework based on the seq2seq architecture, which can solve scalability issues that exist in such methods. Experimental results on the public dataset natural Question demonstrate the effectiveness of this approach. © 2023 IEEE.

关键词： Scalability

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Efficiency Optimization of Large-Scale language Models Based on Deep Learning in natural language processing Tasks 2

Efficiency Optimization of Large-Scale Language Models Based...

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2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Mei, Taiyuan Zi, Yun Cheng, Xiaohan Gao, Zijun Wang, Qi Yang, Haowei Northeastern University Boston United States Georgia Institute of Technology Atlanta United States University of Houston Houston United States

ISBN: (纸本)9798350373646

The internal structure and operation mechanism of large-scale language models are analyzed theoretically, especially how Transformer and its derivative architectures can restrict computing efficiency while capturing long-term dependencies. Further, we dig deep into the efficiency bottleneck of the training phase, and evaluate in detail the contribution of adaptive optimization algorithms (such as AdamW), massively parallel computing techniques, and mixed precision training strategies to accelerate convergence and reduce memory footprint. By analyzing the mathematical principles and implementation details of these algorithms, we reveal how they effectively improve training efficiency in practice. In terms of model deployment and inference optimization, this paper systematically reviews the latest advances in model compression techniques, focusing on strategies such as quantification, pruning, and knowledge distillation. By comparing the theoretical frameworks of these techniques and their effects in different application scenarios, we demonstrate their ability to significantly reduce model size and inference delay while maintaining model prediction accuracy. In addition, this paper critically examines the limitations of current efficiency optimization methods, such as the increased risk of overfitting, the control of performance loss after compression, and the problem of algorithm generality, and proposes some prospects for future research. In conclusion, this study provides a comprehensive theoretical framework for understanding the efficiency optimization of large-scale language models. © 2024 IEEE.

关键词： Risk perception

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Autonomous Machine Learning-Based Peer Reviewer Selection System 31

Autonomous Machine Learning-Based Peer Reviewer Selection Sy...

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31st international conference on Computational Linguistics, COLING 2025

作者： Aitymbetov, Nurmukhammed Zorbas, Dimitrios Nazarbayev University School of Engineering & Digital Sciences Astana Kazakhstan

ISBN: (纸本)9798891761988

The peer review process is essential for academic research, yet it faces challenges such as inefficiencies, biases, and limited access to qualified reviewers. This paper introduces an autonomous peer reviewer selection system that employs the natural language processing (NLP) model to match submitted papers with expert reviewers independently of traditional journals and conferences. Our model performs competitively in comparison with the transformer-based state-of-the-art models while being 10 times faster at inference and 7 times smaller, which makes our platform highly scalable. Additionally, with our paper-reviewer matching model being trained on scientific papers from various academic fields, our system allows scholars from different backgrounds to benefit from this automation. ©2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Towards an Approach for Extending BPMN with Quality Dimension 3rd

Towards an Approach for Extending BPMN with Quality Dimensio...

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3rd international conference on language processing and knowledge Management-LPKM

作者： Alyani, Zohra Turki, Mohamed Dhouib, Karima Gargouri, Faiez MIRACL Lab FSEG Sfax Sfax Tunisia Univ Hail Colledge Comp Sci & Engn Hail 81451 Saudi Arabia ISIM Sfax MIRACL Lab Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

Building a quality management system (QMS) relies heavily on two key steps: mapping and modeling business processes. The process approach, a core principle of QMS, emphasizes identifying and managing processes and their interactions systematically. This ensures achieving desired outcomes aligned with the organization's policies and strategic goals. Following this approach, organizations need to establish, implement, maintain, and continuously improve their quality management system (SQM), encompassing all relevant processes and their connections, to meet regulatory requirements. This article aims to present our approach for extending the BPMN formalism by a quality dimension based on the MDA approach and on an ontological approach for the definition of a meta-model for the quality management which covers the ISO 9001 Norm.

关键词： Quality Management Business process modeling Ontology Core quality ISO 9001 Ontological engineering BPMN2.0

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Quantum Linguistics in Patient-Clinical Trial Relations for Improved Healthcare

Quantum Linguistics in Patient-Clinical Trial Relations for ...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Chandani Sandhu, Ramandeep Islam, Sardar M. N. Lovely Professional University School of Computer Science & Engineering Phagwara India Victoria University Applied Informatics Research Melbourne Australia

ISBN: (纸本)9798331508685

The healthcare industry is undergoing a fundamental revolution with the integration of Quantum natural language processing techniques for matching patient-clinical trials. This research envisions a paradigm where patients and clinical trials collaborate, changing the way healthcare is supplied and medical research is conducted. Apart from this, the research focuses on engaging patients in clinical trials. Patient involvement refers to the process by which patients and their healthcare providers collaborate to make decisions regarding their health. Within the framework of clinical studies, this may entail participants providing input on study designs or monitoring their data through wearable technology and smartphone apps. Quantum NLP has the potential to enhance patient outcomes and advance medical knowledge more quickly by enabling more precise and effective clinical trials. By setting the framework for future applications of quantum NLP, this study expands the conversation on the connection between quantum computing and healthcare. © 2025 IEEE.

关键词： Clinical research

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Data-Driven Pruning Algorithm Based on Result Orientation 5

Data-Driven Pruning Algorithm Based on Result Orientation

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5th international conference on natural language processing, ICNLP 2023

作者： Wu, Jin Zhang, Zhaoqi Zhao, Bo Wang, Yu Xi'an University of Posts and Telecommunication Dept.School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350302219

With their outstanding performance in natural language processing tasks such as machine translation and semantic recognition, deep neural networks have attracted great attention from both academia and industry. For more complex NLP tasks, people try to add more parameters to the network, expand more layers, input larger data samples, to produce a large model to solve the complex task. However, it is not the case that the deeper the layers, the better the parameters. There is a large amount of redundant information in the parameters, which not only contributes nothing to the results, but also increases the computational burden of the model and the storage burden of the hardware. Eliminating a small amount of redundant information often has no effect on the recognition rate of the model, but slightly improves it [1], so the neural network model needs to be compressed. Existing compression methods include model pruning, parameter quantization, tensor decomposition, knowledge distmation, etc. [2] In this paper, model pruning algorithm is selected to implement a result-oriented data-driven pruning algorithm by introducing the propagation characteristics and inter-layer correlation of neural networks and automatic decision making based on Feature Map information. Finally, the effectiveness of the result - oriented data - driven pruning algorithm is proved by comparative experiments. © 2023 IEEE.

关键词： Decision making

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From Generalist to Specialist: A Survey of Large language Models for Chemistry 31

From Generalist to Specialist: A Survey of Large Language Mo...

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31st international conference on Computational Linguistics, COLING 2025

作者： Han, Yang Wan, Ziping Chen, Lu Yu, Kai Chen, Xin X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761964

Large language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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