Synthetic data for annotation and extraction of family history information from clinical text

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441648" target="_blank" >RIV/00216208:11320/21:10441648 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=x9kP.xEylo" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=x9kP.xEylo</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13326-021-00244-2" target="_blank" >10.1186/s13326-021-00244-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Synthetic data for annotation and extraction of family history information from clinical text

Popis výsledku v původním jazyce

Background The limited availability of clinical texts for Natural Language Processing purposes is hindering the progress of the field. This article investigates the use of synthetic data for the annotation and automated extraction of family history information from Norwegian clinical text. We make use of incrementally developed synthetic clinical text describing patients&apos; family history relating to cases of cardiac disease and present a general methodology which integrates the synthetically produced clinical statements and annotation guideline development. The resulting synthetic corpus contains 477 sentences and 6030 tokens. In this work we experimentally assess the validity and applicability of the annotated synthetic corpus using machine learning techniques and furthermore evaluate the system trained on synthetic text on a corpus of real clinical text, consisting of de-identified records for patients with genetic heart disease. Results For entity recognition, an SVM trained on synthetic data had class weighted precision, recall and F-1-scores of 0.83, 0.81 and 0.82, respectively. For relation extraction precision, recall and F-1-scores were 0.74, 0.75 and 0.74. Conclusions A system for extraction of family history information developed on synthetic data generalizes well to real, clinical notes with a small loss of accuracy. The methodology outlined in this paper may be useful in other situations where limited availability of clinical text hinders NLP tasks. Both the annotation guidelines and the annotated synthetic corpus are made freely available and as such constitutes the first publicly available resource of Norwegian clinical text.

Název v anglickém jazyce

Synthetic data for annotation and extraction of family history information from clinical text

Popis výsledku anglicky

Background The limited availability of clinical texts for Natural Language Processing purposes is hindering the progress of the field. This article investigates the use of synthetic data for the annotation and automated extraction of family history information from Norwegian clinical text. We make use of incrementally developed synthetic clinical text describing patients&apos; family history relating to cases of cardiac disease and present a general methodology which integrates the synthetically produced clinical statements and annotation guideline development. The resulting synthetic corpus contains 477 sentences and 6030 tokens. In this work we experimentally assess the validity and applicability of the annotated synthetic corpus using machine learning techniques and furthermore evaluate the system trained on synthetic text on a corpus of real clinical text, consisting of de-identified records for patients with genetic heart disease. Results For entity recognition, an SVM trained on synthetic data had class weighted precision, recall and F-1-scores of 0.83, 0.81 and 0.82, respectively. For relation extraction precision, recall and F-1-scores were 0.74, 0.75 and 0.74. Conclusions A system for extraction of family history information developed on synthetic data generalizes well to real, clinical notes with a small loss of accuracy. The methodology outlined in this paper may be useful in other situations where limited availability of clinical text hinders NLP tasks. Both the annotation guidelines and the annotated synthetic corpus are made freely available and as such constitutes the first publicly available resource of Norwegian clinical text.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10600 - Biological sciences

Projekt

—

Návaznosti

—

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Biomedical Semantics

ISSN

2041-1480

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

11

Kód UT WoS článku

000673553000001

EID výsledku v databázi Scopus

2-s2.0-85110337384