Medical-Information-Extraction-From-Clincal-Text

This is a project regarding medical information extraction from clinical text using clinical text

NLP IN CLINICAL TEXT

Clinical text is often unstructured and contains a lot of medical jargon and acronyms, making it difficult for traditional NLP models to understand and process. Additionally, clinical text often includes important information such as disease, drugs, patient information, diagnoses, and treatment plans, which require specialized NLP models that can accurately extract and understand this medical information.

Another reason clinical text needs different NLP models is that it contains a large amount of data spread across different sources, such as EHRs, clinical notes, and radiology reports, which need to be integrated. This requires models that can process and understand the text and link and integrate the data across different sources and establish clinically acceptable relationships.

Lastly, clinical text often contains sensitive patient information and needs to be protected by strict regulations such as HIPAA. NLP models used to process clinical text must be able to identify and protect sensitive patient information while still providing useful insights.

CLINICAL TEXT

Medical text data can be obtained from various sources, such as electronic health records (EHRs), medical journals, clinical notes, medical websites, and databases. Some of these sources provide publicly available datasets that can be used for training NLP models, while others may require approval and ethical considerations before accessing the data.