In this paper, we determine the relationships between nursing activities and nurseing conversations based on the principle of maximum entropy. For analysis of the features of nursing activities, we built nursing corpora from actual nursing conversation sets collected in hospitals that involve various information about nursing activities. Ex-nurses manually assigned nursing activity information to the nursing conversations in the corpora. Since it is inefficient and too expensive to attach all information manually, we introduced an automatic nursing activity determination method for which we built models of relationships between nursing conversations and activities. In this paper, we adopted a maximum entropy approach for learning. Even though the conversation data set is not large enough for learning, acceptable results were obtained.

In this paper, we analyze nurses' dialogue and conversation data sets after manual transcriptions and show their features. Recently, medical risk management has been recognized as very important for both hospitals and their patients. To carry out medical risk management, it is important to model nursing activities as well as to collect many accident and incident examples. Therefore, we are now researching strategies of modeling nursing activities in order to understand them (E-nightingale Project). To model nursing activities, it is necessary to collect data of nurses' activities in actual situations and to accurately understand these activities and situations. We developed a method to determine any type of nursing activity from voice data. However we found that our method could not determine several activities because it misunderstood special nursing terms. To improve the accuracy of this method, we focus on analyzing nurses' dialogue and conversation data and on collecting special nursing terms. We have already collected 800 hours of nurses' dialogue and conversation data sets in hospitals to find the tendencies and features of how nurses use special terms such as abbreviations and jargon as well as new terms. Consequently, in this paper we categorize nursing terms according to their usage and effectiveness. In addition, based on the results, we show a rough strategy for building nursing dictionaries.