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  1. Article ; Online: Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework.

    Lybarger, Kevin / Ostendorf, Mari / Thompson, Matthew / Yetisgen, Meliha

    Journal of biomedical informatics

    2021  Volume 117, Page(s) 103761

    Abstract: ... This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus ... which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical ... achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83 ...

    Abstract Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). Our span-based event extraction model outperforms an extractor built on MetaMapLite for the identification of symptoms with assertion values. In a secondary use application, we predicted COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information, to explore the clinical presentation of COVID-19. Automatically extracted symptoms improve COVID-19 prediction performance, beyond structured data alone.
    MeSH term(s) COVID-19/diagnosis ; Electronic Health Records ; Humans ; Information Storage and Retrieval ; Natural Language Processing ; Symptom Assessment
    Language English
    Publishing date 2021-03-26
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2021.103761
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework.

    Lybarger, Kevin / Ostendorf, Mari / Thompson, Matthew / Yetisgen, Meliha

    ArXiv

    2021  

    Abstract: ... This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus ... which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical ... achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83 ...

    Abstract Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). In a secondary use application, we explored the prediction of COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information. The automatically extracted symptoms improve prediction performance, beyond structured data alone.
    Language English
    Publishing date 2021-03-10
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Thesis ; Online: Extracting information from clinical text with limited annotated data

    Lybarger, Kevin James

    2020  

    Abstract: ... and COVID-19, two new annotated corpora are developed: the Social History Annotation Corpus (SHAC) and ... the COVID-19 Annotated Clinical Text (CACT) Corpus. These corpora include detailed, high-quality annotations ... regulations. This work explores the automatic extraction of SDOH and COVID-19 diagnosis, testing, and symptom ...

    Abstract Thesis (Ph.D.)--University of Washington, 2020

    Electronic health record (EHR) data informs decision-making in clinical care; however, EHR data are generally underused for other purposes, including secondary use applications. The need to leverage EHR data, including clinical notes, is highlighted by the COVID-19 pandemic, as clinicians, researchers, and policymakers struggle to understand, treat, and contain a new disease. Secondary use cases for EHR data extend to many research areas related to healthcare effectiveness, epidemiology, and public health. Clinical notes contain many types of patient information that are not well characterized through structured data in the EHR, including social determinants of health (SDOH), symptoms, and other factors relevant to clinical informatics research. These patient data are frequently represented in the clinical narrative, rather than structured data, because structured data entry tools can be time-consuming and free-text entry allows richer descriptions. This text-encoded information can benefit secondary use applications, like large retrospective studies and clinical decision-support systems; however, the key information must first be automatically extracted, creating structured representations from unstructured clinical text. Data driven information extraction models require annotated data for training and evaluation, and annotated clinical data is limited by the high cost of annotation and privacy regulations. This work explores the automatic extraction of SDOH and COVID-19 diagnosis, testing, and symptom information from clinical text. The exploration of SDOH and COVID-19 focus on addressing the challenges associated with the limited availability of annotated clinical text. Here, "limited" is intended to mean a relatively small data set or low resource setting. The primary contributions of this work include the introduction of neural clinical information extraction models, new annotated clinical corpora, a novel active learning framework, and a secondary use application utilizing automatically extracted data. We present state-of-the-art neural information extraction approaches for SDOH and COVID-19 information, specifically designing the data-driven extraction architectures to achieve high performance with limited training data, by using multi-task learning and unsupervised pre-training. The extraction models generate event-based predictions that provide a detailed characterization of SDOH and COVID-19, achieving performance levels comparable to the inter-annotator agreement for several important factors. These information extraction approaches are relevant to a range of clinical data. As part of the exploration of SDOH and COVID-19, two new annotated corpora are developed: the Social History Annotation Corpus (SHAC) and the COVID-19 Annotated Clinical Text (CACT) Corpus. These corpora include detailed, high-quality annotations that characterize SDOH and COVID-19 across multiple dimensions. SHAC is unique in its annotation detail, size, and heterogeneity, and CACT is one of the first corpora with COVID-19 related annotations. These corpora are a substantial contribution to the available resources for training and evaluating machine learning-based extraction models at the University of Washington and for the larger clinical informatics community. In collecting SHAC, we introduced a novel active learning framework that uses a relatively simple text classification task as a proxy for a more complex event extraction task. The framework increased corpus richness and heterogeneity and improved extraction performance, relative to random selection. The largest performance improvements are associated with prominent risk factors, like drug and tobacco use, homelessness, and living with others. To demonstrate the utility of the automatically extracted data, this work presents a secondary use application exploring the prediction of COVID-19 infection. Incorporating automatically extracted symptom data improves COVID-19 infection prediction performance, beyond just using existing structured data.
    Keywords clinical informatics ; data science ; machine learning ; natural language processing ; Electrical engineering ; Computer science ; To Be Assigned ; covid19
    Subject code 006
    Language English
    Publishing country us
    Document type Thesis ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Extracting COVID-19 Diagnoses and Symptoms From Clinical Text

    Lybarger, Kevin / Ostendorf, Mari / Thompson, Matthew / Yetisgen, Meliha

    A New Annotated Corpus and Neural Event Extraction Framework

    2020  

    Abstract: ... This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus ... which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical ... achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83 ...

    Abstract Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). In a secondary use application, we explored the prediction of COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information. The automatically extracted symptoms improve prediction performance, beyond structured data alone.
    Keywords Computer Science - Computation and Language ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2020-12-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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