텍스트마이닝 기법을 활용한 항암화학요법을 받는 암 환자의 전화상담 분석
Analysis of Telephone Counseling of Patients in Chemotherapy Using Text Mining Technique
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Purpose: The purpose of this study was to analyze the frequency and relevance of frequent keywords using text mining analysis for symptom-related telephone counseling of patients undergoing chemotherapy, and to understand the current status and characteristics of the nursing intervention. Methods: 442 cases of telephone counseling of patients undergoing chemotherapy were collected. The symptoms were classified and separated according to the contents of the consultation between the nurse and the counseling participants. Using the python library, frequency words were extracted, and the generation of word co-occurrence matrices was analyzed through social network analysis. Results: For the four cancers to be analyzed (breast, colorectum, stomach, lung), the common frequent words of nurse and counseling participants were ‘medical staff (uilyojin)’, ‘medical treatment (jinlyo)’, ‘treatment (chilyo)’, ‘other hospital (tabyeongwon)’, ‘prescription (cheobang)’. In the analysis of social networks, words with highly betweenness centrality, which appear in common, almost matched those of frequent words. Conclusion: In this study, it was possible to extract the most frequent words by cancer type from the contents of telephone counseling with cancer patients and to understand the current status and context of the actual telephone counseling focusing on each keyword.
Abstract
Purpose: The purpose of this study was to analyze the frequency and relevance of frequent keywords using text mining analysis for symptom-related telephone counseling of patients undergoing chemotherapy, and to understand the current status and characteristics of the nursing intervention. Methods: 442 cases of telephone counseling of patients undergoing chemotherapy were collected. The symptoms were classified and separated according to the contents of the consultation between the nurse and the counseling participants. Using the python library, frequency words were extracted, and the generation of word co-occurrence matrices was analyzed through social network analysis. Results: For the four cancers to be analyzed (breast, colorectum, stomach, lung), the common frequent words of nurse and counseling participants were ‘medical staff (uilyojin)’, ‘medical treatment (jinlyo)’, ‘treatment (chilyo)’, ‘other hospital (tabyeongwon)’, ‘prescription (cheobang)’. In the analysis of social networks, words with highly betweenness centrality, which appear in common, almost matched those of frequent words. Conclusion: In this study, it was possible to extract the most frequent words by cancer type from the contents of telephone counseling with cancer patients and to understand the current status and context of the actual telephone counseling focusing on each keyword.
참고문헌 (25)
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