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. 1

A new paradigm, which models the relationships between handwriting and topic categories, in the context of medical forms, is presented. The ultimate goals are (i) the recognition of medical handwriting, and (ii) the use of such information for practical applications such as a medical form search engine. Medical forms have diverse, complex and large lexicons consisting of English, Medical and Pharmacology corpus. Our technique shows that a few recognized characters, returned by handwriting recognition, can be used to construct a linguistic model capable of representing a medical topic

for search engines

I thank the Almighty for providing me with this opportunity to serve Him and make a contribution through His infinite wisdom. I thank my parents for their perseverance and unconditional support, without which I could never have accomplished this endeavor. I would also like to thank other members of my family including my cousin Muneer who has been watching my back from day one. I want to extend my deep appreciation to Dr. Venu Govindaraju, the chair of my dissertation committee. He has been an advisor and a mentor. His persistent guidance, omnipresent motivation and overall support have been the foundation of this thesis. He introduced me to the area of handwriting recognition and encouraged me to address the open challenge of retrieval from handwritten documents. I want to show my gratitude to Dr. Peter Scott, member of my dissertation committee. His course Computer Vision and Image Processing indeed laid a solid foundation for iv this research. His guidance and advise has been always helpful. In addition, I had the opportunity to be his Teaching Assistant for three semesters and his passion for teaching was a great motivation.