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There are a few challenges related to the task of biomedical named entityrecognition, which are: the existing methods consider a fewer number ofbiomedical entities (e.g., disease, symptom, proteins, genes); and thesemethods 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 previousefforts in the following ways: first, it recognizes many biomedical entitytypes other than the standard ones; second, it considers non-clinical factorsrelated to patient's health. This pipeline also consists of stages, such aspreprocessing, tokenization, mapping embedding lookup and named entityrecognition task to extract biomedical named entities from the free texts. Wepresent a new dataset that we prepare by curating the COVID-19 case reports.The proposed approach outperforms the baseline methods on five benchmarkdatasets with macro-and micro-average F1 scores around 90, as well as ourdataset with a macro-and micro-average F1 score of 95.25 and 93.18respectively.

A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities