Experiences implementing scalable, containerized, cloud-based NLP for extracting biobank participant phenotypes at scale
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Updated: Dec 13, 2025
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
Timothy A Miller1,2, Paul Avillach1,3, Kenneth D Mandl1,2
1Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
Objective:
To develop scalable natural language processing (NLP) infrastructure for processing the free text in electronic health records (EHRs).
Materials And Methods:
We extend the open-source Apache cTAKES NLP software with several standard technologies for scalability. We remove processing bottlenecks by monitoring component queue size. We process EHR free text for patients in the PrecisionLink Biobank at Boston Children's Hospital. The extracted concepts are made searchable via a web-based portal.
Results:
We processed over 1.2 million notes for over 8000 patients, extracting 154 million concepts. Our largest tested configuration processes over 1 million notes per day.
Discussion:
The unique information represented by extracted NLP concepts has great potential to provide a more complete picture of patient status.
Conclusion:
NLP large EHR document collections can be done efficiently, in service of high throughput phenotyping.
