Analysis of evaluations of living donors for liver transplantation utilizing text mining approach

利用文本挖掘方法分析活体肝移植供者的评价

基本信息

批准号：
21792322
负责人：
SHIMIZU Junichi
金额：
$ 1.75万
依托单位：
Tokyo Metropolitan University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Young Scientists (B)
财政年份：
2009
资助国家：
日本
起止时间：
2009 至 2010
项目状态：
已结题

项目摘要

The purpose of this study was to analyze the free answers of the investigation about the living donor liver transplantation (LDLT) donor which collected in 2004, utilizing text mining which had become popular in late years.We used two software for text mining ; True Teller (TT : Nomura Research Institute Ltd.) and IBM SPSS Text Analysis for Surveys (STAfS : IBM). We mainly analyzed frequencies and co-occurrences of words in free answers, and evaluated usability and limitations of software in analyzing procedures.Text mining about anxiety about health showed that "vague", "future", "anxiety", "recent" and "nothing" had high frequencies and co-occurrences. It suggested that they felt a vague uneasiness for future health without present symptoms. "Vitality" and "fatigue", and "intestine" and "obstruction" also have them, and these three groups were recognized different from each other. Their anxieties included not only vague but specific.On the other hand, the dictionary function including the synonym registration had an influence on the analysis when survey data from the non-health profession which includes various expressions of nearly same phenomenon.In both analytical procedures of TT and STAfS, the making of dictionaries for analyses took a lot of time, and results of them were similar. But TT had automatic user interface from data import to output graph, STAfS was a tool for preparing dataset for statistic analysis using IBM SPSS Statistics and had high flexibility for following analysis.Using an objective procedure, text mining could visually grasp frequency and the relationship of the word that was hard to distinguish from a large quantity of text data, and there was significance to use it for future analyses of free answers.

这项研究的目的是分析有关生物供体肝移植（LDLT）捐赠者的免费答案，该调查使用了2004年收集的供体，利用文本挖掘在后期很流行。我们使用了两个软件进行文本挖掘； True Teller（TT：Nomura Research Institute Ltd.）和IBM SPSS调查文本分析（STAFS：IBM）。我们主要分析了自由答案中单词的频率和同时出现，并评估了分析程序中软件的可用性和局限性。关于健康焦虑的文本挖掘表明，“模糊”，“未来”，“焦虑”，“最近”和“ Nothing”和“ Nothing”具有高频和同时发生。它表明，他们对未来健康感到模糊的不安，而没有当前的症状。 “活力”和“疲劳”，“肠”和“阻塞”也有它们，这三组被认为彼此不同。他们的焦虑不仅包括含糊，而且包括具体。另一方面，当非健康职业的调查数据包括几乎相同现象的各种表达式时，包括同义词登记在内的字典函数对分析产生了影响。但是TT具有从数据导入到输出图的自动用户界面，Stafs是一种使用IBM SPSS统计数据来准备统计分析数据集的工具，并且具有较高的灵活性来进行以下分析。使用客观过程，文本挖掘可能会掌握频率，并且该单词的关系很难与文本数据的大量文本数据进行区分，并且可以为未来的分析提供重要的答案。