World Journal of Engineering Research and Technology

Devanshi Nayak* and Prof. S. R. Yadav

ABSTRACT

Text data mining is a process of extracting interesting and nontrivial patterns from huge amount of text documents. There exist different techniques and tools to mine the text and discover valuable information for future prediction and decision making process. The selection of right and appropriate text mining technique helps to enhance the speed and decreases the time and effort required to extract valuable information. Care Life Log is used to integrate and analyze the level of care required. There are five levels of care, with Level 1 vocabulary including recreation, toilet, morning, afternoon, etc. The level of care gradually increases from Level 1 to Level 5, which has vocabulary that includes tube, danger, treatment, removal, and discovery. The higher the level, the worse the health condition and therefore the greater care required. These levels allow for a clear analysis of a patient?s condition. This analysis has led to an improvement in Quality of Life as well as a decrease in mismatches between the level of care required for patients and the level of care given by care takers. Rapid progress in digital data acquisition techniques have led to huge volume of data. More than 80 percent of today?s data is composed of unstructured or semi-structured data. The discovery of appropriate patterns and trends to analyze the text documents from massive volume of data is a big issue. A text data mining technique identified the relations between feature vocabularies seen in past in-patient records accumulated on the University of Miyazaki Hospital?s Electronic Medical Record, and extractions were made. The qualitative analysis result of in-patient nursing records used a text data mining technique to achieve the initial goal: a visual record of such information. The analysis discovered vocabularies relating to proper treatment methods and concisely summarized their extracts from in-patient nursing records. Important vocabularies that characterize each nursing record were also revealed. The results of this research will contribute to nursing work evaluation and education.

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