Abstract
Despite the volume increase of electronic data collection in the health area, there is still much medical information that is recorded without any systematic pattern. For instance, besides the structured admission notes format, there are free text fields for clinicians’ patient evaluation observation. Intelligent Decisions Support Systems can benefit from cross-referencing and interpretation of these documents. In the Intensive Care Units, several patients are admitted daily, and several discharge notes are written. To support real-time decision-making and to increase the quality of its process, is crucial to have all relevant patient clinical data available. Since there is no writing pattern followed by all medical doctors, its analysis becomes quite difficult to do. This project aims to make qualitatively and quantitatively analysis of clinical information focusing on the stroke or cerebrovascular accident diagnosis using text analysis tools, namely Natural Language Processing and Text Mining. Our results revealed a set of related words in the clinician’ patient diaries that can reveal patterns.
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Acknowledgements
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-000026.
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Vieira, M., Portela, F., Santos, M.F. (2019). Detecting Automatic Patterns of Stroke Through Text Mining. In: Cortez, P., Magalhães, L., Branco, P., Portela, C., Adão, T. (eds) Intelligent Technologies for Interactive Entertainment. INTETAIN 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 273. Springer, Cham. https://doi.org/10.1007/978-3-030-16447-8_6
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