A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities
This work addresses the problem of limited entity coverage in biomedical text analysis for researchers and healthcare applications, but it is incremental as it builds on existing pipelines with new data and entity types.
The paper tackles biomedical named entity recognition by expanding entity types to include non-clinical factors like social determinants of health and using a new COVID-19 dataset, achieving F1 scores around 90 on benchmarks and over 93 on their dataset.
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.