Cross-narrative Temporal Ordering of Medical Events

Raghavan, Preethi; Fosler‐Lussier, Eric; Elhadad, Noémie; Lai, Albert M.

doi:10.3115/v1/p14-1094

Cited by 21 publications

(18 citation statements)

References 24 publications

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“…Within-sentence analysis can then be used to perform sentence classification in online health communities to detect the presence of adverse drug reactions [32] or to categorize a sentence as conveying emotional or informational support to other users [23]. • Finally, at the discourse level, an emerging line of research addresses the extraction of complete clinical timelines across documents [22,33].…”

Section: Foundational Methods In Clinical Nlpmentioning

confidence: 99%

Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare

Névéol

Zweigenbaum²

2015

Yearb Med Inform

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SummaryObjective: To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP). Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that the field is tackling text analysis methods of increasing depth. The full review process highlighted five papers addressing foundational methods in clinical NLP using clinically relevant texts from online forums or encyclopedias, clinical texts from Electronic Health Records, and included studies specifically aiming at a practical clinical outcome. The increased access to clinical data that was made possible with the recent progress of de-identification paved the way for the scientific community to address complex NLP problems such as word sense disambiguation, negation, temporal analysis and specific information nugget extraction. These advances in turn allowed for efficient application of NLP to clinical problems such as cancer patient triage. Another line of research investigates online clinically relevant texts and brings interesting insight on communication strategies to convey health-related information. Conclusions: The field of clinical NLP is thriving through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques for concrete healthcare purposes. Clinical NLP is becoming mature for practical applications with a significant clinical impact.

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Section: Foundational Methods In Clinical Nlpmentioning

confidence: 99%

Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare

Névéol

Zweigenbaum²

2015

Yearb Med Inform

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“…Extraction of cohort characteristics from clinical texts ranges from the relatively simple task of identifying a single eligibility criterion [30] to the identification of several criteria within or across trials [31,32]. Methods to augment the extracted information and represent the patient in a more sophisticated fashion have also been proposed [33].…”

Section: Extracting Cohort Characteristics From Clinical Textsmentioning

confidence: 99%

“…Because much of the eligibility criteria have a temporal aspect, Raghavan and colleagues argued that the matching of a patient's report to the criteria needs to integrate the timing of events documented in the record [33]. Thus, they proposed an approach to creating a timeline of a patient in a supervised fashion.…”

Section: Extracting Cohort Characteristics From Clinical Textsmentioning

confidence: 99%

Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing

Demner‐Fushman

Elhadad²

2016

Yearb Med Inform

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SummaryObjectives: This paper reviews work over the past two years in Natural Language Processing (NLP) applied to clinical and consumer-generated texts. Methods: We included any application or methodological publication that leverages text to facilitate healthcare and address the health-related needs of consumers and populations. Results: Many important developments in clinical text processing, both foundational and task-oriented, were addressed in community-wide evaluations and discussed in corresponding special issues that are referenced in this review. These focused issues and in-depth reviews of several other active research areas, such as pharmacovigilance and summarization, allowed us to discuss in greater depth disease modeling and predictive analytics using clinical texts, and text analysis in social media for healthcare quality assessment, trends towards online interventions based on rapid analysis of health-related posts, and consumer health question answering, among other issues. Conclusions: Our analysis shows that although clinical NLP continues to advance towards practical applications and more NLP methods are used in large-scale live health information applications, more needs to be done to make NLP use in clinical applications a routine widespread reality. Progress in clinical NLP is mirrored by developments in social media text analysis: the research is moving from capturing trends to addressing individual health-related posts, thus showing potential to become a tool for precision medicine and a valuable addition to the standard healthcare quality evaluation tools.

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“…Their 2014 paper (Raghavan et al, 2014) expanded on this idea to identify medical events spanning across narratives, such as admission notes, medical reports, and discharge notes. Yoshikawa et al (2011) exploited coreference information for extracting eventargument relations from biomedical texts in the Genia Event Corpus.…”

Section: Automated Coreference Resolutionmentioning

confidence: 99%

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

2016

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an interdisciplinary forum for researchers interested in automated processing of health documents. Health documents encompass electronic health records, clinical guidelines, spontaneous reports for pharmacovigilance, biomedical literature, health forums/blogs or any other type of health-related documents. The LOUHI workshop series fosters interactions between the Computational Linguistics, Medical Informatics and Artificial Intelligence communities. Following the six previous edition of the workshop which were co-located with SMBM 2008 in Turku, Finland, with NAACL 2010 in Los Angeles, California, with Artificial Intelligence in Medicine (AIME 2011) in Bled, Slovenia, during NICTA Techfest 2013 in Sydney, Australia, co-located with EACL 2014 in Gothenburg, Sweden, and with EMNLP 2015 in Lisbon, Portugal, this workshop is co-located this year with EMNLP 2016 in Austin, Texas.The aim of the LOUHI 2016 workshop is to bring together research work on topics related to health documents, particularly emphasizing multidisciplinary aspects of health documentation and the interplay between nursing and medical sciences, information systems, computational linguistics and computer science. The topics include, but are not limited to, the following Natural Language Processing techniques and related areas:• Techniques supporting information extraction, e.g. named entity recognition, negation and uncertainty detection• Classification and text mining applications (e.g. diagnostic classifications such as ICD-10 and nursing intensity scores) and problems (e.g. handling of unbalanced data sets)• Text representation, including dealing with data sparsity and dimensionality issues• Domain adaptation, e.g. adaptation of standard NLP tools (incl. tokenizers, PoS-taggers, etc) to the medical domain• Information fusion, i.e. integrating data from various sources, e.g. structured and narrative documentation• Unsupervised methods, including distributional semantics• Evaluation, gold/reference standard construction and annotation• Syntactic, semantic and pragmatic analysis of health documents• Anonymization/de-identification of health records and ethics• Supporting the development of medical terminologies and ontologies• Individualization of content, consumer health vocabularies, summarization and simplification of text• NLP for supporting documentation and decision making practices iii• Predictive modeling of adverse events, e.g. adverse drug events and hospital acquired infectionsThe call for papers encouraged authors to submit papers describing substantial and completed work but also focus on a contribution, a negative result, a software package or work in progress. We also encouraged to report work on low-resourced languages, addressing the challenges of data sparsity and language characteristic diversity.We received 21 submissions. Each submission went through a double-blind review process which involved three program committee members. Based on comments and rankings supplied by the reviewers, we accepted 13 papers. The overall acceptanc...

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Cross-narrative Temporal Ordering of Medical Events

Cited by 21 publications

References 24 publications

Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare

Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare

Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing

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

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