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Extraction and analysis of risk factors from Chinese chemical accident reports

Chinese Journal of Chemical Engineering 2023年第9期61卷 68-81页

作者： Xi Luo Xiayuan Feng Xu Ji Yagu Dang Li Zhou Kexin Bi Yiyang Dai School of Chemical Engineering Sichuan UniversityChengdu 610065China

Accidents in chemical production usually result in fatal injury,economic loss and negative social *** accident reports which record past accident information,contain a large amount of expert ***,manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports,which is time-consuming and labor ***,in this paper,a semiautomatic method based on natural language process(NLP)technology is developed to construct a knowledge graph of chemical ***,we build a named entity recognition(NER)model using SoftLexicon(simplify the usage of lexicon)+BERT-Transformer-CRF(conditional random field)to automatically extract the accident information and risk *** risk factors leading to accident in chemical accident reports are divided into five categories:human,machine,material,management,and *** analysis of the extraction results of different chemical industries and different accident types,corresponding accident prevention suggestions are ***,based on the definition of classes and hierarchies of information in chemical accident reports,the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description ***,the ontology knowledge description model is imported into the graph database Neo4j,and the knowledge graph is constructed to realize the structu red storage of chemical accident *** the case of information extraction from 290 Chinese chemical accident reports,SoftLexicon+BERT-Transformer-CRF shows the best extraction performance among nine experimental *** that the method developed in the current work can be a promising tool in obtaining the factors causing accidents,which contributes to intelligent accident analysis and auxiliary accident prevention.

关键词： Chemical processes Chemical process safety natural language process Knowledge graph Neural networks Algorithm

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Sememe knowledge computation:a review of recent advances in application and expansion of sememe knowledge bases

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Frontiers of Computer Science 2021年第5期15卷 13-23页

作者： Fanchao QI Ruobing XIE Yuan ZANG Zhiyuan LIU Maosong SUN Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute for Artificial Intelligence Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Beijing 100084China Search Product Center WeChat Search Application DepartmentTencentBeijing 100080China Beijing Academy of Artificial Intelligence Beijing 100191China

A sememe is defined as the minimum semantic unit of languages in *** knowledge bases are built by manually annotating sememes for words and *** is the most well-known sememe knowledge *** has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using *** the era of deep learning,although data are thought to be of vital importance,there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system *** successful attempts have been made in the tasks including word representation learning,language modeling,semantic composition,*** addition,considering the high cost of manual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge ***,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new *** this paper,we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.

关键词： natural language process semantics knowledge base sememe HowNet

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Improved diagnosis of thyroid cancer aided with deep learning applied to sonographic text reports: a retrospective, multi-cohort, diagnostic study

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Cancer Biology & Medicine 2022年第5期19卷 733-741页

作者： Qiang Zhang Sheng Zhang Jianxin Li Yi Pan Jing Zhao Yixing Feng Yanhui Zhao Xiaoqing Wang Zhiming Zheng Xiangming Yang Lixia Liu Chunxin Qin Ke Zhao Xiaonan Liu Caixia Li Liuyang Zhang Chunrui Yang Na Zhuo Hong Zhang Jie Liu Jinglei Gao Xiaoling Di Fanbo Meng Wei Ji Meng Yang Xiaojie Xin Xi Wei Rui Jin Lun Zhang Xudong Wang Fengju Song Xiangqian Zheng Ming Gao Kexin Chen Xiangchun Li Department of Maxillofacial and Otorhinolaryngology Oncology National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Key Laboratory of Cancer Prevention and Therapy of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Department of Diagnostic and Therapeutic Ultrasonography National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Key Laboratory of Cancer Prevention and Therapy of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Department of Ultrasonography Weihai Municipal Hospital Cheeloo College of Medicine Shandong University Department of Pathology National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Key Laboratory of Cancer Prevention and Therapy of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Department of Ultrasonography Affiliated Hospital of Chifeng University Department of Ultrasonography Integrated Traditional Chinese and Western Medicine Hospital of Jilin city Jilin Province Department of Ultrasonography Dezhou Municipal Hospital Department of Ultrasound Room of Functions Branch Affiliated Hospital of Hebei University Department of Thyroid and Breast Surgery Weihai Municipal Hospital Cheeloo College of Medicine Shandong University Department of General Surgery Tianjin Medical University General Hospital Department of Thyroid and Breast Surgery Tianjin 4th Centre Hospital Department of Thyroid Surgery Affiliated Hospital of Chengde Medical University Department of Pathology The Second Hospital of Tianjin Medical University Department of Ultrasonography The Second Hospital of Tianjin Medical University Department of Thyroid the Second Hospital of Jilin University Department of Head and Neck Thyroid Surgery Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine of Hebei Province De

Objective: Large volume radiological text data have been accumulated since the incorporation of electronic health record(EHR) systems in clinical practice. We aimed to determine whether deep natural language processing algorithms could aid radiologists in improving thyroid cancer ***: Sonographic EHR data were obtained from the EHR database. Pathological reports were used as the gold standard for diagnosing thyroid cancer. We developed thyroid cancer diagnosis based on natural language processing(THCa Dx NLP) to interpret unstructured sonographic text reports for thyroid cancer diagnosis. We used the area under the receiver operating characteristic curve(AUROC) as the primary metric to measure the performance of the THCa Dx NLP. We compared the performance of thyroid ultrasound radiologists aided with THCa Dx NLP vs. those without THCa Dx NLP using 5 independent test ***: We obtained a total number of 788,129 sonographic radiological reports. The number of thyroid sonographic data points was 132,277, 18,400 of which were thyroid cancer patients. Among the 5 test sets, the numbers of patients per set were 439, 186, 82, 343, and 171. THCa Dx NLP achieved high performance in identifying thyroid cancer patients(the AUROC ranged from 0.857–0.932). Thyroid ultrasound radiologists aided with THCa Dx NLP achieved significantly higher performances than those without THCa Dx NLP in terms of accuracy(93.8% vs. 87.2%; one-sided t-test, adjusted P = 0.003), precision(92.5% vs. 86.0%; P = 0.018), and F1 metric(94.2% vs. 86.4%; P = 0.007).Conclusions: THCa Dx NLP achieved a high AUROC for the identification of thyroid cancer, and improved the accuracy, sensitivity, and precision of thyroid ultrasound radiologists. This warrants further investigation of THCa Dx NLP in prospective clinical trials.

关键词： Thyroid cancer sonographic text report deep learning natural language process

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