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Cardiovascular Disease Prediction Using Langchain

Cardiovascular Disease Prediction Using Langchain

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Advances in Computing, Communication and Applied Informatics (ACCAI), international conference on

作者： K Ananthajothi Joshua David A Kavin Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

While traditional methods struggle to capture the full picture from electronic health records (EHRs), LangChain, a deep learning architecture, emerges as a promising tool for cardiovascular disease (CVD) prediction. By seamlessly integrating natural language processing (NLP) and traditional machine learning, LangChain delves into the rich tapestry of clinical notes, extracting crucial insights often hidden within the text. This, combined with its ability to handle diverse data types and capture temporal relationships, allows LangChain to create a comprehensive understanding of individual health profiles. This not only translates to improved CVD prediction accuracy but also unlocks valuable insights into the disease’ s progression, empowering healthcare professionals with the knowledge to implement early interventions and ultimately improve patient care and outcomes.

关键词： Accuracy Refining Deep architecture Medical services Machine learning natural language processing Cardiovascular diseases

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Dynamic Data Sampler for Cross-language Transfer Learning in Large language Models

Dynamic Data Sampler for Cross-Language Transfer Learning in...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yudong Li Yuhao Feng Wen Zhou Zhe Zhao Linlin Shen Cheng Hou Xianxu Hou School of Computer Science and Software Engineering Shenzhen University Tencent AI Lab LIESMARS Wuhan University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool

Large language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for languages other than English poses significant challenges, due to the difficulty in acquiring large-scale corpus and the requisite computing resources. In this paper, we propose ChatFlow, a cross-language transfer-based LLM, to address these challenges and train large Chinese language models in a cost-effective manner. We employ a mix of Chinese, English, and parallel corpus to continuously train the LLaMA2 model, aiming to align cross-language representations and facilitate the knowledge transfer specifically to the Chinese language model. In addition, we use a dynamic data sampler to progressively transition the model from unsupervised pre-training to supervised fine-tuning. Experimental results demonstrate that our approach accelerates model convergence and achieves superior performance. We evaluate ChatFlow on popular Chinese and English benchmarks, the results indicate that it outperforms other Chinese models post-trained on LLaMA-2-7B.

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Research on I-PageRank algorithm model of Process knowledge graph based on K-Shell decomposition algorithm

Research on I-PageRank algorithm model of Process knowledge ...

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natural language processing (ICNLP), international conference on

作者： Yanwei Huo Hongyu Cheng School of Computing Xi’an University of Posts and Telecommunications Xi’an China School of Electro-Mechanical Engineering Xidian University Xi’an China

PageRank algorithm in the calculation of nodes is equally distributed to the node chain of all nodes, but in the actual production of manufacturing enterprises, the importance of process knowledge in process documents is different, if according to the PageRank algorithm PR value equal transfer to calculate the importance of the artifact, efficiency and accuracy is generally low, so the importance of PR value transfer difference should be considered. Therefore, this paper introduces K-Shell decomposition algorithm in PageRank algorithm, constructs a new I-PageRank algorithm model, adding the importance of each node in the linked network to the PageRank algorithm, which improves the efficiency and accuracy of PageRank algorithm in identifying key nodes.

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Geo-Qwen: A Geometry Problem-Solving Method Based on Generative Large language Models and Heuristic Reasoning

Geo-Qwen: A Geometry Problem-Solving Method Based on Generat...

引用

international conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Yan Shengyuan Zhong Xiuqin School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Research on geometry problem solving is gaining increasing attention in the field of natural language processing (NLP). Currently, research in geometry problem solving often relies on formal logical language systems and traditional rule-based reasoning. However, the transformation from natural language to formal language inevitably leads to information loss, which impacts the accuracy and completeness of reasoning. In recent years, with the development of large language models (LLM) exemplified by the GPT architecture and significant improvements in natural language comprehension, research on purely natural language-based geometric reasoning has become increasingly popular. This paper presents a novel heuristic method for geometric reasoning, called Geo-Qwen, which enables the automatic generation of geometry problem-solving steps from natural language to natural language. To support reasoning, we introduce a geometric theorem library composed of 38 theorems as the reasoning knowledge base. Geo-Qwen employs a heuristic single-step reasoning strategy, automatically generating the next solution step based on a selected theorem, previously generated steps, and the problem information. The generation process of the solution steps can be viewed as constructing a reasoning tree. Additionally, to address the combinatorial explosion issue during reasoning, we utilize the T5 model as a theorem predictor to limit the number of theorems used at each step of reasoning. Finally, extensive experiments conducted on the UniGeo dataset and the Geometry3K dataset demonstrate that Geo-Qwen significantly outperforms existing methods.

关键词： Geometry Accuracy Large language models Predictive models Cognition natural language processing Mathematical models Libraries Multilingual Problem-solving

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Defect Correction Method for Software Requirements Text Using Large language Models

Defect Correction Method for Software Requirements Text Usin...

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international Joint conference on Neural Networks (IJCNN)

作者： Li Zhang Liubo Ouyang Zhuoqun Xu College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Text defect correction holds a pivotal role within the domain of text processing. In the face of the rapid advancement of natural language processing (NLP) technologies, significant challenges persist in achieving precision and efficiency, particularly within specialized domains such as software requirements. For instance, traditional methods require labor-intensive verification, and the generative Large language Models(LLMs) paradigm suffers from illusions and knowledge deficits. So for professional software requirement text and defect correction processing, we construct a Chinese requirement text defect corpus containing 215 Chinese requirement texts and 10,049 manually labeled sentences and propose a contextual prompt pattern based on LLMs, which mainly includes the following steps: (1) Requirements text defect detection classification using fine-tuned BERT. (2) Extracting primary key information from requirement text using fine-tuned UIE. (3) The above results are embedded as contextual information in extensible prompt templates, respectively, and defects are corrected by interacting with generative LLMs. The experimental results show that by utilizing prompt learning and in-context learning techniques for requirement text defect correction, we achieved an outstanding improvement (30.3%) in defect correction effectiveness.

关键词： Large language models Neural networks Software natural language processing Performance analysis Data mining Requirements engineering

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Evaluation of LLMs on Long-tail Entity Linking in Historical Documents 24

Evaluation of LLMs on Long-tail Entity Linking in Historical...

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Joint of Posters, Demos, Workshops, and Tutorials of the 24th international conference on knowledge engineering and knowledge Management, EKAW-PDWT 2024

作者： Boscariol, Marta Bulla, Luana Draetta, Lia Fiumanò, Beatrice Lenzi, Emanuele Piano, Leonardo Department of Management University of Turin Italy Department of Computer Science University of Catania Italy Department of Computer Science University of Turin Italy Department of Modern Languages Literatures and Cultures University of Bologna Italy Department of Information Engineering (DII) University of Pisa Italy Department of Mathematics and Computer Science University of Cagliari Italy Institute of Information Science and Technologies (ISTI) National Research Council of Italy (CNR) Pisa Italy

Entity Linking (EL) plays a crucial role in natural language processing (NLP) applications, enabling the disambiguation of entity mentions by linking them to their corresponding entries in a reference knowledge base (KB). Thanks to their deep contextual understanding capabilities, LLMs offer a new perspective to tackle EL, promising better results than traditional methods. Despite the impressive generalization capabilities of LLMs, linking less popular, long-tail entities remains challenging as these entities are often underrepresented in training data and knowledge bases. Furthermore, the long-tail EL task is an understudied problem, and limited studies address it with LLMs. In the present work, we assess the performance of two popular LLMs, GPT and LLama3, in a long-tail entity linking scenario. Using MHERCL v0.1, a manually annotated benchmark of sentences from domain-specific historical texts, we quantitatively compare the performance of LLMs in identifying and linking entities to their corresponding Wikidata entries against that of ReLiK, a state-of-the-art Entity Linking and Relation Extraction framework. Our preliminary experiments reveal that LLMs perform encouragingly well in long-tail EL, indicating that this technology can be a valuable adjunct in filling the gap between head and long-tail EL. Copyright © 2024 for this paper by its authors.

关键词： Entity linking Historical Documents Large language models Long-tail entities

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Few-Shot Table-to-Text Generation with Structural Bias Attention 20th

Few-Shot Table-to-Text Generation with Structural Bias Atten...

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20th Pacific Rim international conference on Artificial Intelligence, PRICAI 2023

作者： Liu, Di Wang, Weihua Bao, Feilong Gaov, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9789819970216

Table-to-text generation task refers to converting tabular data into language text to facilitate easier understanding and analysis of the table. Recently, pre-trained models have made significant advancements in this kind of tasks. However, the inherent structural differences between tabular data and text, and the lack of domain-specific knowledge in few-shot datasets, make it challenging for pre-trained models to generate faithful text. To solve these problems, we proposed a framework that encodes tables by obtaining structural bias attention through pruning full self-attention, distinguishing the importance of cells from a structural perspective. We use the pre-trained model with the structural bias framework to the generation component of Prototype-to-Generation. To encourage prototype memory to adhere to the table content and generate more accurate and aligned sentences, we employ Reinforcement Learning. We conducted extensive experiments on three few-shot table datasets. Compared to previous advanced methods, our model achieved superior performance across multiple evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reinforcement learning

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Emotional knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhongquan Jian Yancheng Wang Weichao Wu Junfeng Yao Meihong Wang Qingqiang Wu School of Informatics Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China School of Film Xiamen University Xiamen China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in natural language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional knowledge Self-Distillation (EmoKSD 1 ), to enhance the model’s emotional sensitivity. In EmoKSD, utterances are enriched with implicit ⟨mask⟩ tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit ⟨mask⟩ tokens, thereby enhancing the model’s ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC.

关键词： Representation learning Human computer interaction Emotion recognition Sensitivity Speech recognition Signal processing natural language processing Acoustics Speech processing

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Research on Named Entity Recognition Based on Prompt Learning 3

Research on Named Entity Recognition Based on Prompt Learnin...

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3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Zhou, Qing Yong, Tso Lamao, Dongzhi Cairang, Jiacuo College of Information Science and Technology Tibet University China Key Laboratory of Tibetan Information Technology and Artificial Intelligence of Tibet China Engineering Research Center for Tibetan Language Information Technology under the Ministry of Education Lhasa Tibet850000 China

ISBN: (纸本)9798350331387

Named Entity Recognition is the identification of named entities from a sequence of text. The pre-training language model is widely used in natural language processing tasks, but the named entity recognition task is different from the pre-training model task, so the pre-training model cannot be fully used. This article proposes a named entity recognition method based on prompt learning. Different templates are inserted into the input, and the pre-trained language model is used to transform the named entity recognition problem into a mask prediction problem. The mask prediction ability of the model is utilized to predict different mask positions, compensating for the different effects of the named entity recognition task and the pre-trained model task. External knowledge is integrated into the category mapping. Expanding the range of mapping words to improve recognition rate. Named entity recognition experiments were conducted on two datasets, WNUT16 and WNUT17. The appropriate template and category mapping words led to an F1 score of 68.96% in the WNUT16 dataset, which increased by 9.46 percentage points. The precision and recall rates were 64.42% and 67.72%, respectively. In the WNUT17 dataset, the F1 score was 69.62%, increasing by 9.17 percentage points. The precision and recall rates were 73.39% and 71.51%, respectively. © 2023 IEEE.

关键词： Mapping

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A Review on Deep Learning Based Question Answering with natural language processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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knowledge engineering and Communication Systems (ICKECS), international conference on

作者： Abdul Saleem Javeed C Gopala Krishnan Dept. of Computer Science & Engineering GITAM School of Technology Bengaluru INDIA

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics.

关键词： Deep learning Surveys Reviews Soft sensors Pipelines Medical services Question answering (information retrieval)

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