实验室检查结果的解释及其简单表示

T. Okumura, Yuka Tateisi
{"title":"实验室检查结果的解释及其简单表示","authors":"T. Okumura, Yuka Tateisi","doi":"10.1109/CBMS.2013.6627788","DOIUrl":null,"url":null,"abstract":"Knowledge about the causal relationship between diseases and their laboratory findings is a key component for clinical decision support systems. For efficient acquisition of such knowledge, this paper attempted to represent the interpretation of laboratory results in a guidebook for laboratory examinations. A preliminary survey revealed the structure of the knowledge compiled in the guidebook, and found essential patterns in the cause-effect relationship. We then attempted to code the knowledge, utilizing a simple cause-effect relationship between exam results and their possible causes, expressed in a disease master table. For coding of the knowledge, a two-step approach was used: first the causing disease was looked up automatically in the disease master table, and then, manually. In the study, 84.5% of the knowledge in the guidebook was identified as a candidate for the coding in the simple cause-effect relationship, and 69.1% of the knowledge was successfully coded. Failure analysis suggested that further expressive power in the representation is gained only at the cost of considerable human intervention in the knowledge acquisition, and the cost for the utilization of the resulting data. Accordingly, for a certain type of application, which might prefer simplicity over accuracy or completeness of the information, the minimalist representation could be a reasonable choice.","PeriodicalId":20519,"journal":{"name":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","volume":"45 1","pages":"197-202"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Interpretation of laboratory examination results and their simple representation\",\"authors\":\"T. Okumura, Yuka Tateisi\",\"doi\":\"10.1109/CBMS.2013.6627788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Knowledge about the causal relationship between diseases and their laboratory findings is a key component for clinical decision support systems. For efficient acquisition of such knowledge, this paper attempted to represent the interpretation of laboratory results in a guidebook for laboratory examinations. A preliminary survey revealed the structure of the knowledge compiled in the guidebook, and found essential patterns in the cause-effect relationship. We then attempted to code the knowledge, utilizing a simple cause-effect relationship between exam results and their possible causes, expressed in a disease master table. For coding of the knowledge, a two-step approach was used: first the causing disease was looked up automatically in the disease master table, and then, manually. In the study, 84.5% of the knowledge in the guidebook was identified as a candidate for the coding in the simple cause-effect relationship, and 69.1% of the knowledge was successfully coded. Failure analysis suggested that further expressive power in the representation is gained only at the cost of considerable human intervention in the knowledge acquisition, and the cost for the utilization of the resulting data. Accordingly, for a certain type of application, which might prefer simplicity over accuracy or completeness of the information, the minimalist representation could be a reasonable choice.\",\"PeriodicalId\":20519,\"journal\":{\"name\":\"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems\",\"volume\":\"45 1\",\"pages\":\"197-202\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2013.6627788\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2013.6627788","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

了解疾病及其实验室结果之间的因果关系是临床决策支持系统的关键组成部分。为了有效地获取这些知识,本文试图在实验室检查指南中表示实验室结果的解释。初步调查揭示了指南中所编知识的结构,并发现了因果关系的基本模式。然后,我们尝试对知识进行编码,利用考试结果与其可能原因之间的简单因果关系,用疾病控制表表示。对于知识的编码,采用了两步的方法:首先在疾病主控表中自动查找致病疾病,然后手动查找致病疾病。在本研究中,84.5%的指南知识被确定为简单因果关系编码的候选知识,69.1%的指南知识被成功编码。失败分析表明,进一步提高表达能力的代价是在知识获取过程中大量的人为干预,以及对结果数据的利用成本。因此,对于某些类型的应用程序,它可能更喜欢简单性而不是信息的准确性或完整性,极简表示可能是一个合理的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretation of laboratory examination results and their simple representation
Knowledge about the causal relationship between diseases and their laboratory findings is a key component for clinical decision support systems. For efficient acquisition of such knowledge, this paper attempted to represent the interpretation of laboratory results in a guidebook for laboratory examinations. A preliminary survey revealed the structure of the knowledge compiled in the guidebook, and found essential patterns in the cause-effect relationship. We then attempted to code the knowledge, utilizing a simple cause-effect relationship between exam results and their possible causes, expressed in a disease master table. For coding of the knowledge, a two-step approach was used: first the causing disease was looked up automatically in the disease master table, and then, manually. In the study, 84.5% of the knowledge in the guidebook was identified as a candidate for the coding in the simple cause-effect relationship, and 69.1% of the knowledge was successfully coded. Failure analysis suggested that further expressive power in the representation is gained only at the cost of considerable human intervention in the knowledge acquisition, and the cost for the utilization of the resulting data. Accordingly, for a certain type of application, which might prefer simplicity over accuracy or completeness of the information, the minimalist representation could be a reasonable choice.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.10
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信