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A multi-modal dialogue analysis method for medical interviews based on design of interaction corpus.

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Title: A multi-modal dialogue analysis method for medical interviews based on design of interaction corpus.
Authors: Koyama, Yuichi1 ykoyama@nagoya-u.jp, Sawamoto, Yuichi2, Hirano, Yasushi3 hirano@nagoya-u.jp, Kajita, Shoji3 kajita@nagoya-u.jp, Mase, Kenji1 mase@nagoya-u.jp, Suzuki, Tomio4, Katsuyama, Kimiko5, Yamauchi, Kazunobu6
Source: Personal & Ubiquitous Computing. Dec2010, Vol. 14 Issue 8, p767-778. 12p. 8 Diagrams, 5 Charts.
Subjects: Dialogue analysis, Medical history taking, Physicians, Nonverbal communication, Indexes
Abstract: We propose a multi-modal dialogue analysis method for medical interviews that hierarchically interprets nonverbal interaction patterns in a bottom-up manner and simultaneously visualizes the topic structure. Our method aims to provide physicians with the clues generally overlooked by conventional dialogue analysis to form a cycle of dialogue practice and analysis. We introduce a motif and a pattern cluster in the designs of the hierarchical indices of interaction and exploit the Jensen-Shannon divergence (JSD) metric to reduce the number of usable indices. We applied the proposed interpretation method of interaction patterns to develop a corpus of interviews. The results of a summary reading experiment confirmed the validity of the developed indices. Finally, we discussed the integrated analysis of the topic structure and a nonverbal summary. [ABSTRACT FROM AUTHOR]
Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Koyama%2C+Yuichi%22">Koyama, Yuichi</searchLink><relatesTo>1</relatesTo><i> ykoyama@nagoya-u.jp</i><br /><searchLink fieldCode="AR" term="%22Sawamoto%2C+Yuichi%22">Sawamoto, Yuichi</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hirano%2C+Yasushi%22">Hirano, Yasushi</searchLink><relatesTo>3</relatesTo><i> hirano@nagoya-u.jp</i><br /><searchLink fieldCode="AR" term="%22Kajita%2C+Shoji%22">Kajita, Shoji</searchLink><relatesTo>3</relatesTo><i> kajita@nagoya-u.jp</i><br /><searchLink fieldCode="AR" term="%22Mase%2C+Kenji%22">Mase, Kenji</searchLink><relatesTo>1</relatesTo><i> mase@nagoya-u.jp</i><br /><searchLink fieldCode="AR" term="%22Suzuki%2C+Tomio%22">Suzuki, Tomio</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Katsuyama%2C+Kimiko%22">Katsuyama, Kimiko</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Yamauchi%2C+Kazunobu%22">Yamauchi, Kazunobu</searchLink><relatesTo>6</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Personal+%26+Ubiquitous+Computing%22">Personal & Ubiquitous Computing</searchLink>. Dec2010, Vol. 14 Issue 8, p767-778. 12p. 8 Diagrams, 5 Charts.
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  Data: We propose a multi-modal dialogue analysis method for medical interviews that hierarchically interprets nonverbal interaction patterns in a bottom-up manner and simultaneously visualizes the topic structure. Our method aims to provide physicians with the clues generally overlooked by conventional dialogue analysis to form a cycle of dialogue practice and analysis. We introduce a motif and a pattern cluster in the designs of the hierarchical indices of interaction and exploit the Jensen-Shannon divergence (JSD) metric to reduce the number of usable indices. We applied the proposed interpretation method of interaction patterns to develop a corpus of interviews. The results of a summary reading experiment confirmed the validity of the developed indices. Finally, we discussed the integrated analysis of the topic structure and a nonverbal summary. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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