Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers–Assisted Sublanguage Analysis

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

A patient’s family history (FH) information significantly influences downstream clinical care. Despite this importance, there is no standardized method to capture FH information in electronic health records and a substantial portion of FH information is frequently embedded in clinical notes. This renders FH information difficult to use in downstream data analytics or clinical decision support applications. To address this issue, a natural language processing system capable of extracting and normalizing FH information can be used.

Objective

In this study, we aimed to construct an FH lexical resource for information extraction and normalization.

Methods

We exploited a transformer-based method to construct an FH lexical resource leveraging a corpus consisting of clinical notes generated as part of primary care. The usability of the lexicon was demonstrated through the development of a rule-based FH system that extracts FH entities and relations as specified in previous FH challenges. We also experimented with a deep learning–based FH system for FH information extraction. Previous FH challenge data sets were used for evaluation.

Results

The resulting lexicon contains 33,603 lexicon entries normalized to 6408 concept unique identifiers of the Unified Medical Language System and 15,126 codes of the Systematized Nomenclature of Medicine Clinical Terms, with an average number of 5.4 variants per concept. The performance evaluation demonstrated that the rule-based FH system achieved reasonable performance. The combination of the rule-based FH system with a state-of-the-art deep learning–based FH system can improve the recall of FH information evaluated using the BioCreative/N2C2 FH challenge data set, with the F1 score varied but comparable.

Conclusions

The resulting lexicon and rule-based FH system are freely available through the Open Health Natural Language Processing GitHub.

Related collections

Most cited references 27

Record: found
Abstract: found
Article: found

Is Open Access

The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2016)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

0 comments Cited 2969 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee … (2018)

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

0 comments Cited 518 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

Ozlem Uzuner, Brett R South, Shuying Shen … (2011)

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.

0 comments Cited 95 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Hongfang Liu:

ORCID: https://orcid.org/0000-0003-2570-3741

Department of Artificial Intelligence and InformaticsMayo Clinic200 First Street SWRochester, MN, 55905United States1 507 293 0057liu.hongfang@mayo.edu

Journal

Journal ID (nlm-ta): JMIR Med Inform

Journal ID (iso-abbrev): JMIR Med Inform

Journal ID (publisher-id): JMI

Title: JMIR Medical Informatics

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-9694

Publication date Collection: 2023

Publication date (Electronic): 27 June 2023

Volume: 11

Electronic Location Identifier: e48072

Affiliations

[1 ] Department of Artificial Intelligence and Informatics Mayo Clinic Rochester, MN United States

[2 ] Center for Digital Health Mayo Clinic Rochester, MN United States

[3 ] Department of Computer Science University of Kentucky Lexington, KY United States

[4 ] Division of Biomedical Informatics Department of Internal Medicine University of Kentucky Lexington, KY United States

Author notes

Corresponding Author: Hongfang Liu liu.hongfang@ 123456mayo.edu

Author information

Liwei Wang https://orcid.org/0000-0001-9970-8604

Huan He https://orcid.org/0000-0003-1312-4195

Andrew Wen https://orcid.org/0000-0001-9090-8028

Sungrim Moon https://orcid.org/0000-0002-9191-3897

Sunyang Fu https://orcid.org/0000-0003-1691-5179

Kevin J Peterson https://orcid.org/0000-0002-9758-4609

Xuguang Ai https://orcid.org/0009-0000-2432-8316

Sijia Liu https://orcid.org/0000-0001-9763-1164

Ramakanth Kavuluru https://orcid.org/0000-0003-1238-9378

Hongfang Liu https://orcid.org/0000-0003-2570-3741

Article

Publisher ID: v11i1e48072

DOI: 10.2196/48072

PMC ID: 10337517

PubMed ID: 37368483

SO-VID: 2928f08e-e47d-4b55-be30-6e992a6b5bab

Copyright © ©Liwei Wang, Huan He, Andrew Wen, Sungrim Moon, Sunyang Fu, Kevin J Peterson, Xuguang Ai, Sijia Liu, Ramakanth Kavuluru, Hongfang Liu. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.06.2023.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 13 April 2023

Date revision requested : 12 May 2023

Date revision received : 25 May 2023

Date accepted : 1 June 2023

Submit your digital health research with an established publisher
- celebrating 25 years of open access