Implementing a portable clinical NLP system with a common data model: a Lisp perspective
Name
1811.06179.pdf
Description
Submitted version
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755.05 KB
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Adobe PDF
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869a840283214b98212858fdf19609fd
Author(s)
Luo, Yuan
Szolovits, Peter
Date Issued
January 2019
Journal
IEEE International Conference on Bioinformatics and Biomedicine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Luo, Yuan, and Peter Szolovits, "Implementing a portable clinical NLP system with a common data model: a Lisp perspective." Proceedings, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), December 3-6, 2018, Madrid, Spain (Piscataway, N.J.: IEEE, 2018): doi 10.1109/BIBM.2018.8621521 ©2018 Author(s)
Version
Original manuscript
Abstract
This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP. ©2018 IEEE. Keywords: portable NLP; computational phenotyping; relation extraction; common data model; Lisp
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/BIBM.2018.8621521
