Abstract
Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but also in other clinical domains. In our previous work, we have suggested a general framework for performing this task, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge. We have recently developed a prototype system, CliniText, which demonstrates our ideas. Our prototype combines knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data. We demonstrate both our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating a discharge summary of a patient from the MIMIC database, who had undergone a Coronary Artery Bypass Graft operation.
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References
Goldstein, A., Shahar, Y.: A Framework for Automated Knowledge-Based Textual Summarization of Longitudinal Medical Records. In: KR4HC Workshop, Tallinn, Estonia (2012)
Combi, C., Shahar, Y., Keravnou-Papailiou, E.: Temporal Information Systems in Medicine. Springer, New York (2010)
Portet, F., Reiter, E., Hunter, J., Sripada, S.: Automatic generation of textual summaries from neonatal intensive care data. AI in Med., 227–236 (2007)
Shahar, Y.: A framework for knowledge-based temporal abstraction. AI 90, 79–133 (1997)
Lavrač, N., Kononenko, I., Keravnou, E., Kukar, M., Zupan, B.: Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Communications 11, 191–218 (1999)
McDonald, D.D., Bolc, L.: Natural Language Generation Systems. Springer, New York (1988)
Huske-Kraus, D.: Text generation in clinical medicine-a review. Meth. Info. Med. 42, 51–60 (2003)
Portet, F., et al.: Automatic generation of textual summaries from neonatal intensive care data. Artificial Intelligence (2009), doi:10.1016/j.artint.2008.12.002
Hatsek, A., Shahar, Y., Taieb-Maimon, M., Shalom, E., Klimov, D., Lunenfeld, E.: A scalable architecture for incremental specification and maintenance of procedural decision-support knowledge. Open. Med. Info., 255–277 (2010)
Boaz, D., Shahar, Y.: Idan: A distributed temporal-abstraction mediator for medical databases. AI in Medicine, 21–30 (2003)
Owens, D., Sox, H.: Biomedical Decision Making: Probabilistic Clinical Reasoning. In: Biomedical Informatics (Shortliffe and Cimino), ch. 3, pp. 80–129 (2006)
The Laboratory for Computational Physiology, http://www.physionet.org
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Goldstein, A., Shahar, Y. (2013). Implementation of a System for Intelligent Summarization of Longitudinal Clinical Records. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds) Process Support and Knowledge Representation in Health Care. ProHealth KR4HC 2013 2013. Lecture Notes in Computer Science(), vol 8268. Springer, Cham. https://doi.org/10.1007/978-3-319-03916-9_6
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DOI: https://doi.org/10.1007/978-3-319-03916-9_6
Publisher Name: Springer, Cham
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