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Your search keyword '"Tomohiro KURODA"' showing total 14 results
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14 results on '"Tomohiro KURODA"'

13. Prioritizing Features to Redesign in an EMR System.

Author

Helou, Samar, Abou-Khalil, Victoria, Goshiro Yamamoto, Eiji Kondoh, Hiroshi Tamura, Shusuke Hiragi, Osamu Sugiyama, Kazuya Okamoto, Nambu, Masayuki, and Tomohiro Kuroda

Subjects
ELECTRONIC health records, USER-centered system design, PROJECT management, PRENATAL care, MEDICAL decision making
Abstract

Redesigning Electronic Medical Record (EMR) systems is needed to improve their usefulness and usability. For user-centered redesign, designers should consider which EMR features are the most important to the users. However, prioritizing the EMR features is complicated because: (i) EMR systems involve multiple users with different, and sometimes conflicting, priorities and (ii) targeting one feature will affect other features of the EMR system. In this work, we propose a method for prioritizing the features to target when redesigning an EMR system. The method takes into consideration the different priorities of the users and the relationships between the different features. We illustrate the method through a case study on redesigning EMR systems in Japanese antenatal care settings. Our results show the importance of considering the different types of EMR users and the relationships between different EMR features. Designers could use the proposed method as a decision-aid tool in EMR redesign projects. [ABSTRACT FROM AUTHOR]

Published
2019
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14. Early Nephrosis Detection ased on Deep Learning with Clinical Time-Series Data.

Author

Yohei Yamasaki, Osamu Sugiyama, Shusuke Hiragia, Shosuke Ohteraa, Goshiro Yamamoto, Hiroshi Sasaki, Kazuya Okamotoa, Masayuki Nambu, and Tomohiro Kuroda

Subjects
DEEP learning, ABNORMAL proteins, DECISION support systems, MACHINE learning, MEDICAL care
Abstract

Nephrosis is disease characterized by abnormal protein loss from impaired kidney. We constructed early prediction model using machine learning from clinical time series data, that can predict onset of nephrosis for more than one month. Long shortterm memory capable of recognizing temporal sequential data patterns, was adopted as early prediction model for nephrosis. We verified our proposed prediction model has higher accuracy compared with those of baseline classifiers by 5-fold cross validation. [ABSTRACT FROM AUTHOR]

Published
2019
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