Intelligent Technologies for Interactive Entertainment. 10th EAI International Conference, INTETAIN 2018, Guimarães, Portugal, November 21-23, 2018, Proceedings

Research Article

Detecting Automatic Patterns of Stroke Through Text Mining

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  • @INPROCEEDINGS{10.1007/978-3-030-16447-8_6,
        author={Miguel Vieira and Filipe Portela and Manuel Santos},
        title={Detecting Automatic Patterns of Stroke Through Text Mining},
        proceedings={Intelligent Technologies for Interactive Entertainment. 10th EAI International Conference, INTETAIN 2018, Guimar\"{a}es, Portugal,  November 21-23, 2018, Proceedings},
        proceedings_a={INTETAIN},
        year={2019},
        month={4},
        keywords={Medical information Admission notes Intelligent Decisions Support Systems Intensive Care Units},
        doi={10.1007/978-3-030-16447-8_6}
    }
    
  • Miguel Vieira
    Filipe Portela
    Manuel Santos
    Year: 2019
    Detecting Automatic Patterns of Stroke Through Text Mining
    INTETAIN
    Springer
    DOI: 10.1007/978-3-030-16447-8_6
Miguel Vieira1, Filipe Portela1,*, Manuel Santos1,*
  • 1: University of Minho
*Contact email: cfp@dsi.uminho.pt, mfs@dsi.uminho.pt

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.