Background : Knowledge is evolving over time, often as a result of new
discoveries or changes in the adopted methods of reasoning. Also, new facts or
evidence may become available, leading to new understandings of complex
phenomena. This is particularly true in the biomedical field, where scientists
and physicians are constantly striving to find new methods of diagnosis,
treatment and eventually cure. Knowledge Graphs (KGs) offer a real way of
organizing and retrieving the massive and growing amount of biomedical
knowledge.
Objective : We propose an end-to-end approach for knowledge extraction and
analysis from biomedical clinical notes using the Bidirectional Encoder
Representations from Transformers (BERT) model and Conditional Random Field
(CRF) layer.
Methods : The approach is based on knowledge graphs, which can effectively
process abstract biomedical concepts such as relationships and interactions
between medical entities. Besides offering an intuitive way to visualize these
concepts, KGs can solve more complex knowledge retrieval problems by
simplifying them into simpler representations or by transforming the problems
into representations from different perspectives. We created a biomedical
Knowledge Graph using using Natural Language Processing models for named entity
recognition and relation extraction. The generated biomedical knowledge graphs
(KGs) are then used for question answering.
Results : The proposed framework can successfully extract relevant structured
information with high accuracy (90.7% for Named-entity recognition (NER), 88%
for relation extraction (RE)), according to experimental findings based on
real-world 505 patient biomedical unstructured clinical notes.
Conclusions : In this paper, we propose a novel end-to-end system for the
construction of a biomedical knowledge graph from clinical textual using a
variation of BERT models