Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss abo...Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.展开更多
Evidence-based medicine(EBM)is recognized as one of the highest-quality scientific approaches in the medical community around the globe.It calls for doctors to use the best available scientific evidence in clinical de...Evidence-based medicine(EBM)is recognized as one of the highest-quality scientific approaches in the medical community around the globe.It calls for doctors to use the best available scientific evidence in clinical decision-making.This paper used an empirical study on 44 EBM related judicial cases in China,the result shows the EBM is commonly used as a supplement to the expert opinion in actual judicial review,it is deemed to illuminate the causation in the case fact rather than as the standards of care,which has the similar characteristics as"documentary evidence",and over the years the Chinese judicial practice formed a"three-stage"judicial review rule on EBM:(1)the first stage is whether the evidence itself can meet the standards of EBM;(2)the second stage is when determining the evidence presented by parties is sufficient to meet the legal standards of EBM,and whether it can be applied in a court case would depend upon comprehensive consideration of adaptability and maturity of EBM;(3)the third stage is whether to treat EBM as the only basis in causation analysis.展开更多
通过建立和应用基于临床决策支持系统(Clinical Decision Support System,CDSS)的电子护理文书质量控制录入系统,将护理记录信息项结构化、建立录入信息决策知识库、嵌入提醒与自动生成护理任务功能,保障了电子护理文书录入的规范性、...通过建立和应用基于临床决策支持系统(Clinical Decision Support System,CDSS)的电子护理文书质量控制录入系统,将护理记录信息项结构化、建立录入信息决策知识库、嵌入提醒与自动生成护理任务功能,保障了电子护理文书录入的规范性、完整性和连续性,提高了临床电子护理文书质量,同时也可有效地指导护士的临床决策,从而减少护理差错产生。展开更多
文摘Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.
基金This research was funded by Sichuan Medical Health Legal Research Center Project(grant number:YF21-Q06)Anhui Law and Social Security Research Center Project(grant number:fzsh2021cx-17)National Social Science Foundation Project“Comparative Study on Public Health Legislation”(grant number:20CFX016).
文摘Evidence-based medicine(EBM)is recognized as one of the highest-quality scientific approaches in the medical community around the globe.It calls for doctors to use the best available scientific evidence in clinical decision-making.This paper used an empirical study on 44 EBM related judicial cases in China,the result shows the EBM is commonly used as a supplement to the expert opinion in actual judicial review,it is deemed to illuminate the causation in the case fact rather than as the standards of care,which has the similar characteristics as"documentary evidence",and over the years the Chinese judicial practice formed a"three-stage"judicial review rule on EBM:(1)the first stage is whether the evidence itself can meet the standards of EBM;(2)the second stage is when determining the evidence presented by parties is sufficient to meet the legal standards of EBM,and whether it can be applied in a court case would depend upon comprehensive consideration of adaptability and maturity of EBM;(3)the third stage is whether to treat EBM as the only basis in causation analysis.
文摘通过建立和应用基于临床决策支持系统(Clinical Decision Support System,CDSS)的电子护理文书质量控制录入系统,将护理记录信息项结构化、建立录入信息决策知识库、嵌入提醒与自动生成护理任务功能,保障了电子护理文书录入的规范性、完整性和连续性,提高了临床电子护理文书质量,同时也可有效地指导护士的临床决策,从而减少护理差错产生。
