• Manisha Mali
• Dr. Neeraj Sharma

Medical (also known as clinical) data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data; These categories include radiology reports, pathology investigation reports, Doctor's advice or prescriptions, and many more. Among these reports, radiology reports and pathology reports are the most critical medical documents that a diagnostician looks into, especially in case of emergency. They provide the physicians with crucial information regarding the patient's condition and help them take immediate action in critical situations. However, these reports exist in the form of unstructured text, which makes them time-consuming for humans to interpret. So there is a need to extract the medically necessary information and their importance from these reports. We have proposed a system that will extract the medically significant information in structured form and a level of importance and classify the overall report into critical or non-critical categories, which help doctors identify potential high-priority reports. Our proposed system is based on Bootstrapped Pattern Learning for information extraction and Artificial Intelligence Techniques for classifying data into critical and non-critical categories.

Information Extraction, Clinical data, Bootstrapped Pattern Learning, Artificial Intelligence

"SIGNIFICANT INFORMATION EXTRACTION OF MEDICAL DATA THROUGH BOOTSTRAPPED PATTERN LEARNING AND ARTIFICIAL INTELLIGENCE TECHNIQUES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.c891-c895, July-2021, Available :http://www.jetir.org/papers/JETIR2107386.pdf

"SIGNIFICANT INFORMATION EXTRACTION OF MEDICAL DATA THROUGH BOOTSTRAPPED PATTERN LEARNING AND ARTIFICIAL INTELLIGENCE TECHNIQUES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppc891-c895, July-2021, Available at : http://www.jetir.org/papers/JETIR2107386.pdf