Bt-Nurse: Computer generation of natural language shift summaries from complex heterogeneous medical data

Journal of the American Medical Informatics Association

Volumn 18, Issue 5, 2011, Pages 621-624

  Hunter, James ^a   Freer, Yvonne ^b   Gatt, Albert ^c   Reiter, Ehud ^a   Sripada, Somayajulu ^a   Sykes, Cindy ^b   Westwater, Dave ^d  

^a UNIVERSITY OF ABERDEEN   (United Kingdom)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

^c UNIVERSITY OF MALTA   (Malta)

^d Data2Text   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BT NURSE SYSTEM; COMPUTER PROGRAM; COMPUTER SYSTEM; DATA EXTRACTION; ELECTRONIC MEDICAL RECORD; INFORMATION PROCESSING; MEDICAL DOCUMENTATION; MEDICAL INFORMATION SYSTEM; NATURAL LANGUAGE PROCESSING; NEWBORN INTENSIVE CARE; NURSE;

COMPUTER SYSTEMS; CONTINUITY OF PATIENT CARE; DATA MINING; ELECTRONIC HEALTH RECORDS; HUMANS; INFANT, NEWBORN; INTENSIVE CARE UNITS, NEONATAL; MONITORING, PHYSIOLOGIC; NATURAL LANGUAGE PROCESSING; NURSING RECORDS; SCOTLAND;

EID: 80053285249     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000193     Document Type: Article

References (20)

1. Law AS, Freer Y, Hunter J, et al. A comparison of graphical and textual presentations of time series data to support medical decision making in the neonatal intensive care unit. J Clin Monit Comput 2005;19:183-94.
2. van der Meulen M, Logie RH, Freer Y, et al. When a graph is poorer than 100 words: a comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care. Appl Cognit Psychol 2008;24:77-89.
3. Gatt A, Portet F, Reiter E, et al. From data to text in the neonatal intensive care unit: using NLG technology for decision support and information management. AI Communications 2009;22:153-86.
4. Portet F, Reiter E, Gatt A, et al. Automatic generation of textual summaries from neonatal intensive care data. Artif Intell 2009;173:789-816.
5. Reiter E, Dale R. Building Natural Language Generation Systems. Cambridge: Cambridge University Press, 2000.
6. Reiter E. An architecture for data-to-text systems. In: Buseman S, ed. Proceedings of the 11th European Workshop on Natural Language Generation. Schloss Dagstuhl, Germany: ACL, 2007:97-104.
7. Gatt A, Reiter E. SimpleNLG: a realisation engine for practical applications. In: Krahmer E, Theune M, eds. Twelfth European Workshop on Natural Language Generation: Proceedings of the Workshop. Athens, Greece: ACL, 2009:90-4.
8. Artstein R, Poesio M. Inter-coder agreement for computational linguistics. Comput Ling 2008;34:555-96.
9. Goldberg E, Driedger N, Kittredge RI. Using natural language processing to produce weather forecasts. IEEE Expert 1994;9:45-53.
10. Coch J. Multimeteo: multilingual generation of weather forecasts. ELRA Newsletter 1998;3:13-15.
11. Reiter E, Sripada S, Hunter J, et al. Choosing words in computer-generated weather forecasts. Artif Intell 2005;167:137-69.
12. Turner R, Sripada S, Reiter E, et al. Selecting the content of textual descriptions of geographically located events in spatio-temporal weather data. In: Ellis R, Allen T, Petridis M, eds. Applications and Innovations in Intelligent Systems XV. Proceedings of the 27th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Berlin: Springer, 2007:75-88.
13. Belz A. Automatic generation of weather forecast texts using comprehensive probabilistic generation space models. Nat Lang Eng 2007;14:431-55.
14. Kukich K. Design of a knowledge-based report generator. In: Marcus M, ed. Proceedings of the 21st Annual Meeting of the Association for Computational Linguistics. Cambridge, MA: ACL, 1983:145-50.
15. Iordanskaja L, Kim M, Kittredge R, et al. Generation of extended bilingual statistical reports. In: Proceedings of the 15th International Conference on Computational Linguistics. Nantes: ICCL, 1992:1019-23.
16. Ferres L, Parush A, Roberts S, et al. Helping people with visual impairments gain access to graphical information through natural language: the iGraph system. In: Meisenberger K, Klaus J, Zagler W, et al, eds. Computers Helping People with Special Needs: 10th International Conference ICCHP 2006. Berlin: Springer, 2006:1122-30.
17. Yu J, Reiter E, Hunter J, et al. Choosing the content of textual summaries of large time-series data sets. Nat Lang Eng 2007;13:25-49.
18. Dalal M, Feiner S, McKeown K, et al. MAGIC: an experimental system for generating multimedia briefings about post-bypass patient status. In: Cimino JJ, ed. Proceedings of the AMIA Annual Fall Symposium. Washington, DC: Henley and Belfus Inc, 1996:684-8.
19. Harris M. Building a large-scale commercial NLG system for an EMR. In: White M, Nakatsu C, McDonald D, eds. INLG 2008: Fifth International Natural Language Generation Conference. Salt Fork, OH: ACL, 2008:157-60.
20. Moreland KL. Validation of computer-based test interpretations: problems and prospects. J Consult Clin Psychol 1985;53:816-25.