King, BrianLuo, XiaoGeorge, Susanna SereneWyss, Catharine2021-08-102021-08-102021-08https://hdl.handle.net/1805/26444http://dx.doi.org/10.7912/C2/68Indiana University-Purdue University Indianapolis (IUPUI)Being in the midst of a pandemic with patients having minor symptoms that quickly become fatal to patients with situations like a stemi heart attack, a fatal accident injury, and so on, the importance of medical research to improve speed and efficiency in patient care, has increased. As researchers in the computer domain work hard to use automation in technology in assisting the first responders in the work they do, decreasing the cognitive load on the field crew, time taken for documentation of each patient case and improving accuracy in details of a report has been a priority. This paper presents an information extraction algorithm that custom engineers certain existing extraction techniques that work on the principles of natural language processing like metamap along with syntactic dependency parser like spacy for analyzing the sentence structure and regular expressions to recurring patterns, to retrieve patient-specific information from medical narratives. These concept value pairs automatically populates the fields of an EMR form which could be reviewed and modified manually if needed. This report can then be reused for various medical and billing purposes related to the patient.en-USConcept extractionNatural Language ProcessingEMR-drivenMulti-label classificationThesis