Abstract
BackgroundFamily history information, including information on family members, side of the family of family members, living status of family members, and observations of family members, plays an important role in disease diagnosis and treatment. Family member information extraction aims to extract family history information from semistructured/unstructured text in electronic health records (EHRs), which is a challenging task regarding named entity recognition (NER) and relation extraction (RE), where named entities refer to family members, living status, and observations, and relations refer to relations between family members and living status, and relations between family members and observations.ObjectiveThis study aimed to introduce the system we developed for the 2019 n2c2/OHNLP track on family history extraction, which can jointly extract entities and relations about family history information from clinical text.MethodsWe proposed a novel graph-based model with biaffine attention for family history extraction from clinical text. In this model, we first designed a graph to represent family history information, that is, representing NER and RE regarding family history in a unified way, and then introduced a biaffine attention mechanism to extract family history information in clinical text. Convolution neural network (CNN)-Bidirectional Long Short Term Memory network (BiLSTM) and Bidirectional Encoder Representation from Transformers (BERT) were used to encode the input sentence, and a biaffine classifier was used to extract family history information. In addition, we developed a postprocessing module to adjust the results. A system based on the proposed method was developed for the 2019 n2c2/OHNLP shared task track on family history information extraction.ResultsOur system ranked first in the challenge, and the F1 scores of the best system on the NER subtask and RE subtask were 0.8745 and 0.6810, respectively. After the challenge, we further fine tuned the parameters and improved the F1 scores of the two subtasks to 0.8823 and 0.7048, respectively.ConclusionsThe experimental results showed that the system based on the proposed method can extract family history information from clinical text effectively.
Highlights
IntroductionThe methods mentioned above have been applied for clinical entity recognition and relation extraction (RE), such as the natural language processing (NLP) challenges organized by i2b2 in 2009 [9], 2010 [10], 2012 [11], and 2014 [12], the NLP challenges organized by SemEval in 2015 [13] and 2016 [14], the NLP challenges organized by ShARe/CLEF in 2013 [15] and 2014 [16], and the NLP challenges organized by BioCreative/OHNLP in 2018 [17]
History information plays an important role in the diagnosis and treatment of diseases, especially genetic disorders.Family history information is always embedded in electronic health records (EHRs) in a semistructured/unstructured format, which needs to be unlocked by natural language processing (NLP) technology.https://medinform.jmir.org/2021/4/e23587 XSLFO RenderXJMIR Med Inform 2021 | vol 9 | iss. 4 | e23587 | p. 1In order to promote research on family history information extraction, Harvard Medical School and Mayo Clinic organized national NLP challenges on family history information extraction in 2018 and 2019
The methods mentioned above have been applied for clinical entity recognition and relation extraction (RE), such as the NLP challenges organized by i2b2 in 2009 [9], 2010 [10], 2012 [11], and 2014 [12], the NLP challenges organized by SemEval in 2015 [13] and 2016 [14], the NLP challenges organized by ShARe/CLEF in 2013 [15] and 2014 [16], and the NLP challenges organized by BioCreative/OHNLP in 2018 [17]
Summary
The methods mentioned above have been applied for clinical entity recognition and RE, such as the NLP challenges organized by i2b2 in 2009 [9], 2010 [10], 2012 [11], and 2014 [12], the NLP challenges organized by SemEval in 2015 [13] and 2016 [14], the NLP challenges organized by ShARe/CLEF in 2013 [15] and 2014 [16], and the NLP challenges organized by BioCreative/OHNLP in 2018 [17] Most of these methods process NER and RE tasks in a pipeline way, which can suffer from error propagation [18]. Member information extraction aims to extract family history information from semistructured/unstructured text in electronic health records (EHRs), which is a challenging task regarding named entity recognition (NER) and relation extraction (RE), where named entities refer to family members, living status, and observations, and relations refer to relations between family members and living status, and relations between family members and observations
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