  There are a few challenges related to the task of biomedical named entity
recognition, which are: the existing methods consider a fewer number of
biomedical entities (e.g., disease, symptom, proteins, genes); and these
methods do not consider the social determinants of health (age, gender,
employment, race), which are the non-medical factors related to patients'
health. We propose a machine learning pipeline that improves on previous
efforts in the following ways: first, it recognizes many biomedical entity
types other than the standard ones; second, it considers non-clinical factors
related to patient's health. This pipeline also consists of stages, such as
preprocessing, tokenization, mapping embedding lookup and named entity
recognition task to extract biomedical named entities from the free texts. We
present a new dataset that we prepare by curating the COVID-19 case reports.
The proposed approach outperforms the baseline methods on five benchmark
datasets with macro-and micro-average F1 scores around 90, as well as our
dataset with a macro-and micro-average F1 score of 95.25 and 93.18
respectively.
