Machine learning model for clinical named entity recognition

To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It found the literature that several researchers have extensively machine learning models for NER.The fundamental tasks among medical data mining are and normalization. Medical different general NER in various ways. Huge number of alternate spellings synonyms create explosion word vocabulary sizes. This reduces medicine dictionary efficiency. Entities often consist long sequences tokens, making harder to detect boundaries exactly. The notes written by clinicians less structured minimal grammatical form with cryptic short hand. Because this, it poses challenges recognition. Generally, systems either rule based or pattern based. rules patterns not generalizable because diverse writing style clinicians. use approach resolve these issues focus on choosing effective features classifier building. In this work, has been a required manner