Dataset Information

Natural language processing in aid of FlyBase curators.

ABSTRACT:

Background

Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear.

Results

PaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser. Then, we present how we appraise PaperBrowser's navigational functionalities in a user-based study using a text highlighting task and evaluation criteria of Human-Computer Interaction. Our results show that PaperBrowser reduces the amount of interactions between two highlighting events and therefore improves navigational efficiency by about 58% compared to the navigational mechanism that was previously available to the curators. Moreover, PaperBrowser is shown to provide curators with enhanced navigational utility by over 74% irrespective of the different ways in which they highlight text in the article.

Conclusion

We show that state-of-the-art performance in certain NLP tasks such as Named Entity Recognition and Anaphora Resolution can be combined with the navigational functionalities of PaperBrowser to support curation quite successfully.

SUBMITTER: Karamanis N

PROVIDER: S-EPMC2375127 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature

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Publications

Natural language processing in aid of FlyBase curators.

Karamanis Nikiforos N Seal Ruth R Lewin Ian I McQuilton Peter P Vlachos Andreas A Gasperin Caroline C Drysdale Rachel R Briscoe Ted T

BMC bioinformatics 20080414

<h4>Background</h4>Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear.<h4>Results</h4>PaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser. Then, ...[more]

PMID: 18410678

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Project description:BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information.

Project description:BackgroundHealth researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature.ObjectiveThis scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods.MethodsA systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology.ResultsOf 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies.ConclusionsThe current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured.

Dataset Information

Natural language processing in aid of FlyBase curators.

Background

Results

Conclusion

Publications

Natural language processing in aid of FlyBase curators.

Similar Datasets

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