2010 Fiscal Year Final Research Report
Analysis of evaluations of living donors for liver transplantation utilizing text mining approach
| Project/Area Number |
21792322
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Community health/Gerontological nurisng
|
|---|
| Research Institution | Tokyo Metropolitan University |
|---|
Principal Investigator |
SHIMIZU Junichi Tokyo Metropolitan University, 大学院・人間健康科学研究科, 准教授 (40381462)
|
|---|
| Project Period (FY) |
2009 – 2010
|
| Keywords | 看護学 / 移植・再生医療 / ドナー |
|---|
| Research Abstract |
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.
|
