Medical word recognition using a computational semantic lexicon

Abstract

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.