Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes
DOI:
https://doi.org/10.53469/jcmp.2024.06(08).67Keywords:
Electronic health records, Unstructured Clinical Notes, Natural Language Processing, patient data extraction, SpaCy, healthcare informaticsAbstract
Electronic Health Records (EHRs) are pivotal in modern healthcare, housing a treasure trove of patient information. They are real-time, patient-centered records that make information available instantly and securely to authorized users. However, a substantial portion of this data resides in unstructured clinical notes, presenting significant challenges for data extraction and utilization. This research paper investigates the issues posed by unstructured clinical notes application of Natural Language Processing (NLP) techniques in the healthcare sector to extract structured patient data from unstructured clinical notes. By utilizing NLP algorithms, healthcare institutions can unlock invaluable insights within EHRs, leading to improved patient care, clinical research, and administrative efficiency. This paper addresses various NLP approaches, the implementation of pre-trained SpaCy and Med7Modelfor extracting structural data, and the potential for future advancements in this critical area of healthcare informatics.
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