* a) A Medical Record Creation Support System Using a Voice Memo Recorded by a Mobile Device Hiromitsu NISHIZAKI a), Keisuke KURUMIZAWA, Kanae NISHIZA
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1 * a) A Medical Record Creation Support System Using a Voice Memo Recorded by a Mobile Device Hiromitsu NISHIZAKI a), Keisuke KURUMIZAWA, Kanae NISHIZAKI, and Hitoshi IKEGAMI 1. [1] [2] % 21.7% Graduate School of Interdisciplinary Research, Faculty of Engineering, University of Yamanashi, Takeda, Kofu-shi, Japan Department of Education, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Kofu-shi, Japan Kugawa Hospital for Orthopaedic Surgery, Shinden-cho 10 26, Kofu-shi, Japan a) hnishi@yamanashi.ac.jp * DOI: /transinfj.2015JDP [3] 358 D Vol. J99 D No. 3 pp c 2016
2 [4] [5] Physical Therapist PT PT 2 PT AmiVoice R Ex7 Clinic/HospitalClient 1 amazingcharts.com Amazing Charts 2 Klann [6] PT Neuance Dragon Medical 360 Mobile Recorder 3 AmiVoice R Smart 4 PT mobile-recorder/
3 2016/3 Vol. J99 D No. 3 [7] [7] PT PT PT 360
4 Fig. 1 1 A workflow of a medical record creation. [5] PT PT PT PT PT Apple ipod touch (ios) PT PT 2 Fig. 2 An application for voice memo recording. (Staff s and patient s names are dummy.) [7] [8] 3. 2 [9] [10] (Confusion Network Combination, CNC) [11] [12] [13] [15] CNC 2 361
5 2016/3 Vol. J99 D No. 3 Fig. 4 4 GUI Graphical user interface for editing. 3 Fig. 3 An editor for creating a medical record. Julius [16] 2 ( ) CNC 2 (CN) 5 CN CN 1 CN 3. 3 [11] Pod- Casle [17] Liang [18] CN 3 CNC CN 6-best 4 Graphical User Interface, GUI best words CNC CN (1-best) correction candidates CN 6 (6-best) / Del Temp Del best word Temp 3. 4 Julius
6 Fig. 6 6 An example of fixed phrase where words are replaced by the class. 5 Fig. 5 The interface for adding words and fixed phrases for adapting a language model and a speech recognition dictionary. 55.0% PT 5 GUI N-gram N-gram 6 N-gram [19] 7 Corpus of Spontaneous Japanese, CSJ [20] Palmkit [21] 8 N-gram PT
7 2016/3 Vol. J99 D No. 3 1 PT ([M SS ]) ([%]) Table 1 Record creation execution times ([M SS ] and ASR performance[%]) for PTs. #1 #2 #3 #4 #5 #6 #7 #8 #9 Ave ( ) Apple 5 ipod touch CNC 2 Julius Ver CSJ 3, triphone (Hidden Markov Model, HMM) Julius triphone #5 2 PT Table 2 Questionnaire results (average for all the subjects). (1) 3.4 (2) 3.7 (3) 3.6 (4) #5 90% 85% 90% 3 85% 37 Novice Expert ratio Method (NEM) [22] ( [23]) NE / NE 4.5 NE GUI 364
8 3 ([M SS ]) Table 3 Change of medical record creation times caused by habituation of the system ([M SS ]) # # # GUI #10 # # # PT 5. CNC JSPS (B) [1] 03/h html July 1, [2] JAHIS data list/data0204/ July 1, [3] R. Koppel, J.P. Metlay, A. Cohen, B. Abaluck, A.R. Localio, S.E. Kimmel, and B.L. Strom, Role of computerized physician order entry systems in facilitating eedication errors, J. American Medical Association, vol.293, no.10, pp , Oct [4] D vol.j93-d, no.10, pp , Oct [5] SIG-SLUD-B003-11, pp.57 62, Sept [6] J.G. Klann and P. Szolovits, An intelligent listening framework for capturing encounter notes from a doctor-atient dialog, BMC Medical Information 365
9 2016/3 Vol. J99 D No. 3 and Decision Making 2009, vol.9 (Suppl. 1), pp.1 10, [7] K. Kurumizawa, H. Nishizaki, K. Nishizaki, and H. Ikegami, Input support system for medical records created using a voice memo recorded by a mobile device, Int. J. Signal Process. Syst., vol.3, no.2, pp , Dec [8] 73 3ZF-1, pp , March [9] 3-P-11, pp , Sept [10] L. Mangu, E. Brill, and A. Stolcke, Finding consensus in speech recognition: Word error minimization and other applications of confusion network, Computer Speech and Language, vol.14, no.4, pp , Oct [11] S. Ishimaru, H. Nishizaki, and Y. Sekiguchi, Effect of confusion network combination on speech recognition system for editing, Proc. APSIPA ASC 2011, pp.1 4, Oct [12] CRF 3-P-10, pp , Sept [13] J.G. Fiscus, A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER), Proc. ASRU 1997, pp , Dec [14] G. Evermann and P. Woodland, Posterior probability decoding, confidence estimation and system combination, Proc. NIST Speech Transcription Workshop, pp.55 56, [15] T. Utsuro, Y. Kodama, T. Watanabe, H. Nishizaki, and S. Nakagawa, An empirical study on multiple LVCSR model combination by machine learning, Proc. HLT/NAACL 2004, pp.13 16, May [16] Julius vol.11, no.1, pp.31 38, Jan [17] M. Goto, J. Ogata, and K. Eto, Podcastle: A web 2.0 approach to speech recognition research, Proc. INTERSPEECH 2007, pp , Sept [18] Y. Liang, K. Iwano, and K. Shinoda, An efficient error correction interface for speech recognition on mobile touchscreen devices, Proc. SLT 2014, pp , Dec [19] vol.41, no.11, pp , Nov [20] K. Maekawa, Corpus of Spontaneous Japanese: Its design and evaluation, Proc. SSRP 2003, pp.7 12, April [21] n-gram 2000-SLP-34-32, pp , Dec [22] H. Urokohara, K. Tanaka, K. Furuta, M. Honda, and M. Kurosu, NEM: Novice expert ratio method a usability evaluation method to generate a new performance measure, CHI 00 Extended Abstracts on Human Factors in Computing Systems, pp , Jan [23] vol.8, no.2, pp , Nov IEEE AAOSNew Educational Program Fellow 366
DOI: /transinfj.2015JDP7 早期公開日 :2015/11/18 本 PDF は, 早期公開版である. 本論文を引用する場合には, 電子情報通信学会和文論文誌投稿のしおり ( 情報 システムソサイエティ ) の 8. 早期公開 を参照すること.
DOI:10.14923/transinfj.2015JDP7 早期公開日 :2015/11/18 本 PDF は, 早期公開版である. 本論文を引用する場合には, 電子情報通信学会和文論文誌投稿のしおり ( 情報 システムソサイエティ ) の 8. 早期公開 を参照すること. a) A Medical Record Creation Support System using a Voice Memo
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