No abstract available.
Eructation* ; Speech Therapy*

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.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.