压扁理论:药物行为的预测方法

M. Buscema
{"title":"压扁理论:药物行为的预测方法","authors":"M. Buscema","doi":"10.1300/J023V08N03_08","DOIUrl":null,"url":null,"abstract":"SUMMARY This paper describes a prediction approach called Squashing Theory. Squashing Theory, as used in this example to predict drug behavior, incorporates biological, psychological and sociological measures. The Artificial Feed Forward Neural Network -a recently developed computer architecture inspired by the brain's structure (Dayhoff, 1990)-is the framework for Squashing Theory. The Network was computer programmed by the Semeion Research Center in Rome, Italy. The model was able to predict drug behavior at the 92% level on prototypical cases and at the 80% level on uncertain cases based on self reported drug use from two norming samples on the prediction sample.","PeriodicalId":366329,"journal":{"name":"Drugs in society","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Squashing Theory: A Prediction Approach for Drug Behavior\",\"authors\":\"M. Buscema\",\"doi\":\"10.1300/J023V08N03_08\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SUMMARY This paper describes a prediction approach called Squashing Theory. Squashing Theory, as used in this example to predict drug behavior, incorporates biological, psychological and sociological measures. The Artificial Feed Forward Neural Network -a recently developed computer architecture inspired by the brain's structure (Dayhoff, 1990)-is the framework for Squashing Theory. The Network was computer programmed by the Semeion Research Center in Rome, Italy. The model was able to predict drug behavior at the 92% level on prototypical cases and at the 80% level on uncertain cases based on self reported drug use from two norming samples on the prediction sample.\",\"PeriodicalId\":366329,\"journal\":{\"name\":\"Drugs in society\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drugs in society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1300/J023V08N03_08\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drugs in society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1300/J023V08N03_08","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

摘要

本文描述了一种称为压扁理论的预测方法。压扁理论,就像在这个例子中用来预测药物行为一样,包含了生物学、心理学和社会学的测量方法。人工前馈神经网络是一种受大脑结构启发而发展起来的计算机体系结构(Dayhoff, 1990),它是挤压理论的框架。这个网络是由意大利罗马的塞米恩研究中心用计算机编程的。该模型在预测样本的两个规范化样本中,基于自我报告的药物使用情况,对原型病例的药物行为预测达到92%的水平,对不确定病例的药物行为预测达到80%的水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Squashing Theory: A Prediction Approach for Drug Behavior
SUMMARY This paper describes a prediction approach called Squashing Theory. Squashing Theory, as used in this example to predict drug behavior, incorporates biological, psychological and sociological measures. The Artificial Feed Forward Neural Network -a recently developed computer architecture inspired by the brain's structure (Dayhoff, 1990)-is the framework for Squashing Theory. The Network was computer programmed by the Semeion Research Center in Rome, Italy. The model was able to predict drug behavior at the 92% level on prototypical cases and at the 80% level on uncertain cases based on self reported drug use from two norming samples on the prediction sample.
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
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:481959085
Book学术官方微信