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

本研究的目的是利用近年来流行的文本挖掘技术，对2004年收集的活体肝移植（LDLT）供者调查的自由答案进行分析。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文本分析调查（STAfS: IBM）。我们主要分析了自由答案中单词的频率和共现率，并评估了软件在分析过程中的可用性和局限性。关于健康焦虑的文本挖掘显示，“模糊”、“未来”、“焦虑”、“最近”和“什么都没有”出现的频率很高，而且同时出现。这表明，他们对未来的健康感到模糊的不安，但没有目前的症状。“元气”与“乏力”、“肠”与“梗阻”也有，这三组被认为是不同的。他们的焦虑不仅模糊，而且具体。另一方面，包括同义词登记在内的词典功能对来自非卫生行业的调查数据的分析产生了影响，这些数据包含了几乎相同现象的各种表达。在TT和STAfS的分析过程中，分析词典的编制都花费了大量的时间，而且分析结果是相似的。但TT具有从数据导入到输出图形的自动用户界面，STAfS是使用IBM SPSS Statistics准备数据集进行统计分析的工具，对后续分析具有很高的灵活性。文本挖掘采用一种客观的过程，可以直观地把握大量文本数据中难以区分的词的频率和关系，对今后的自由答案分析具有重要意义。

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.