  Objective: Causality mining is an active research area, which requires the
application of state-of-the-art natural language processing techniques. In the
healthcare domain, medical experts create clinical text to overcome the
limitation of well-defined and schema driven information systems. The objective
of this research work is to create a framework, which can convert clinical text
into causal knowledge. Methods: A practical approach based on term expansion,
phrase generation, BERT based phrase embedding and semantic matching, semantic
enrichment, expert verification, and model evolution has been used to construct
a comprehensive causality mining framework. This active transfer learning based
framework along with its supplementary services, is able to extract and enrich,
causal relationships and their corresponding entities from clinical text.
Results: The multi-model transfer learning technique when applied over multiple
iterations, gains performance improvements in terms of its accuracy and recall
while keeping the precision constant. We also present a comparative analysis of
the presented techniques with their common alternatives, which demonstrate the
correctness of our approach and its ability to capture most causal
relationships. Conclusion: The presented framework has provided cutting-edge
results in the healthcare domain. However, the framework can be tweaked to
provide causality detection in other domains, as well. Significance: The
presented framework is generic enough to be utilized in any domain, healthcare
services can gain massive benefits due to the voluminous and various nature of
its data. This causal knowledge extraction framework can be used to summarize
clinical text, create personas, discover medical knowledge, and provide
evidence to clinical decision making.
