Artificial Intelligence (AI) plays the following two crucial roles in medical form analysis: recognition, as an input, of the New York State (NYS) Prehospital Care Report (PCR), and data inferences as an output. The PCR provides medical, legal, and quality assurance (QA) data (approximately 2-3 Years behind in storage and analysis) that needs to be efficiently centralized to aid health care. Automating NYS PCR analysis will facilitate a more efficient and useful description of a patient being admitted to a hospital emergency room (ER). ER environments can be highly stressful on the human body given the time constraints of bioterrorism, trauma and/or disease. The recognition task will allow these ER health care professionals to evaluate all data and emergency techniques performed by paramedics and emergency medical technicians (EMT's). A computer screen, presenting diagrams, descriptions and inferences of a human body, representing the patient, will be updated with the corresponding handwritten PCR information. This information can then be transported to a central data bank where other hospitals can determine if there are possible outbreaks due to bio-terrorism, disease, hazardous materials incident or other non-obvious mass casualty incidents (MCI). Currently, it may take several days or even weeks, when it is clearly too late, to discover a massive atrocity. The recognition process will involve a method for reducing the size of the lexicon by integrating semantic knowledge with pattern recognition data.


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    Titel :

    Medical word recognition using a computational semantic lexicon


    Beteiligte:
    Milewski, R. (Autor:in) / Govindaraju, V. (Autor:in)


    Erscheinungsdatum :

    2002-01-01


    Format / Umfang :

    968262 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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