A practical approach towards causality mining in clinical text using active transfer learning

Musarrat Hussain, Fahad Ahmed Satti, Jamil Hussain, Taqdir Ali, Syed Imran Ali, Hafiz Syed Muhammad Bilal, Gwang Hoon Park, Sungyoung Lee, Tae Choong Chung

Research output: Contribution to journalArticlepeer-review

Abstract

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 substantial performance improvements. 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.

Original languageEnglish
Article number103932
JournalJournal of Biomedical Informatics
Volume123
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Active transfer learning
  • Causality mining
  • Clinical text mining
  • Machine learning

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