Dependency Parsing for Extracting Family History

Lewis, Neal; Gruhl, Daniel; Yang, Hui

doi:10.1109/hisb.2011.23

Cited by 6 publications

(5 citation statements)

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“…We measure the performance of our parsers in terms of the ability to recover dependencies from biomedical text. Dependency recovery is not only a useful component in processing both clinical text (Lewis et al, 2011;Sohn et al, 2012) and biomedical literature (Seoud and Mabrouk, 2013;Cohen and Elhadad, 2012;Miyao et al, 2008;Poon and Vanderwende, 2010;Qian and Zhou, 2012), it also provides an evaluation metric that is independent of the particular syntactic formalism employed in the parser.…”

Section: Discussionmentioning

confidence: 99%

“…This is particularly true of long range dependencies such as that between activities and decreased in the specific synthetic activities of electrophoretically purified myosin heavy chain decreased. Such dependencies have proven to be useful features in many text mining and knowledge extraction applications, for example identifying biomarkers in the biomedical literature (Seoud and Mabrouk, 2013) or extracting family history from clinical text (Lewis et al, 2011). Correctly identifying the dependencies within a string of words is generally based on finding the most probable structure over them, and this in turn requires knowing what sort of relations each word is likely to enter into.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

Grouin¹,

Hamon²,

Névéol³

et al. 2015

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Cancer stages, which summarizes extent of cancer progression, is an important tool for evidence-based medical research. However, they are not always recorded in the electronic medical record. In this paper, we describe work for annotating a medical text corpus with the goal of predicting patient level liver cancer staging in hepatocellular carcinoma (HCC) patients. Our annotation consisted of identifying 11 parameters, used to calculate liver cancer staging, at the text span level as well as at the patient level. Also at the patient level, we annotated stages for three commonly-used liver cancer staging schemes. Our inter-rater agreement showed text annotation consistency 0.73 F1 for partial text match and 0.91 F1 at the patient level. After annotation, we performed several document classification experiments for the text span annotations using standard machine learning classifiers, including decision trees, maximum entropy, naive Bayes and support vector machines. Thereby, we identified baseline performances for our task at 0.63 F1 as well as strategies for future improvement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

Grouin¹,

Hamon²,

Névéol³

et al. 2015

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“…This system achieved a precision of 0.96 and a recall of 0.93 in family history diagnoses detection, and 0.92 and 0.92, respectively, in specific family member assignment. Lewis et al [23] developed a set of dependency patterns and used the Stanford NLP Parser [24] to map specific family members to specific diseases. This approach was able to achieve a precision of 0.82 and recall of 0.52, but details were not provided regarding their relative lexicon and encoding strategies.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Representation of Information about Family Relatives as Structured Data in Electronic Health Records

Zhou

Vitale

et al. 2014

Appl Clin Inform

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SummaryBackground: The ability to manage and leverage family history information in the electronic health record (EHR) is crucial to delivering high-quality clinical care. Objectives: We aimed to evaluate existing standards in representing relative information, examine this information documented in EHRs, and develop a natural language processing (NLP) application to extract relative information from free-text clinical documents. Methods:We reviewed a random sample of 100 admission notes and 100 discharge summaries of 198 patients, and also reviewed the structured entries for these patients in an EHR system's family history module. We investigated the two standards used by Stage 2 of Meaningful Use (SNOMED CT and HL7 Family History Standard) and identified coverage gaps of each standard in coding relative information. Finally, we evaluated the performance of the MTERMS NLP system in identifying relative information from free-text documents. Results: The structure and content of SNOMED CT and HL7 for representing relative information are different in several ways. Both terminologies have high coverage to represent local relative concepts built in an ambulatory EHR system, but gaps in key concept coverage were detected; coverage rates for relative information in free-text clinical documents were 95.2% and 98.6%, respectively. Compared to structured entries, richer family history information was only available in freetext documents. Using a comprehensive lexicon that included concepts and terms of relative information from different sources, we expanded the MTERMS NLP system to extract and encode relative information in clinical documents and achieved a corresponding precision of 100% and recall of 97.4%. Conclusions: Comprehensive assessment and user guidance are critical to adopting standards into EHR systems in a meaningful way. A significant portion of patients' family history information is only documented in free-text clinical documents and NLP can be used to extract this information.

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“…Although the application of NLP methods and resources to biomedical texts has received increasing attention [6][7][8], with methods for FH extraction [9][10][11], the progress has been limited by difficulties in accessing shared tools and resources, partially caused by patient privacy and data confidentiality constraints. There are some recent efforts to increase the sharing and interoperability of existing resources.…”

Section: Introductionmentioning

confidence: 99%

Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition

Shen¹,

Liu²,

Fu³

et al. 2021

JMIR Med Inform

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Background As a risk factor for many diseases, family history (FH) captures both shared genetic variations and living environments among family members. Though there are several systems focusing on FH extraction using natural language processing (NLP) techniques, the evaluation protocol of such systems has not been standardized. Objective The n2c2/OHNLP (National NLP Clinical Challenges/Open Health Natural Language Processing) 2019 FH extraction task aims to encourage the community efforts on a standard evaluation and system development on FH extraction from synthetic clinical narratives. Methods We organized the first BioCreative/OHNLP FH extraction shared task in 2018. We continued the shared task in 2019 in collaboration with the n2c2 and OHNLP consortium, and organized the 2019 n2c2/OHNLP FH extraction track. The shared task comprises 2 subtasks. Subtask 1 focuses on identifying family member entities and clinical observations (diseases), and subtask 2 expects the association of the living status, side of the family, and clinical observations with family members to be extracted. Subtask 2 is an end-to-end task which is based on the result of subtask 1. We manually curated the first deidentified clinical narrative from FH sections of clinical notes at Mayo Clinic Rochester, the content of which is highly relevant to patients’ FH. Results A total of 17 teams from all over the world participated in the n2c2/OHNLP FH extraction shared task, where 38 runs were submitted for subtask 1 and 21 runs were submitted for subtask 2. For subtask 1, the top 3 runs were generated by Harbin Institute of Technology, ezDI, Inc., and The Medical University of South Carolina with F1 scores of 0.8745, 0.8225, and 0.8130, respectively. For subtask 2, the top 3 runs were from Harbin Institute of Technology, ezDI, Inc., and University of Florida with F1 scores of 0.681, 0.6586, and 0.6544, respectively. The workshop was held in conjunction with the AMIA 2019 Fall Symposium. Conclusions A wide variety of methods were used by different teams in both tasks, such as Bidirectional Encoder Representations from Transformers, convolutional neural network, bidirectional long short-term memory, conditional random field, support vector machine, and rule-based strategies. System performances show that relation extraction from FH is a more challenging task when compared to entity identification task.

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Dependency Parsing for Extracting Family History

Cited by 6 publications

References 7 publications

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

Representation of Information about Family Relatives as Structured Data in Electronic Health Records

Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition

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