Detecting temporal expressions in medical narratives

Reeves, Ruth; Ong, Ferdo R.; Matheny, Michael E.; Denny, Joshua C.; Aronsky, Dominik; Gobbel, Glenn T.; Montella, Diane; Speroff, Theodore; Brown, Steven H.

doi:10.1016/j.ijmedinf.2012.04.006

Cited by 29 publications

(20 citation statements)

References 24 publications

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“…Even though the goal of anonymization proposals is not to analyze or prioritize EMRs, the NLP techniques applied to recognize identifiers and quasi-identifiers are very close to the process of identifying entities for other purposes. Other existing research for EMR text pre-processing has demonstrated the possibility of extracting temporal expressions [32], [33]; correcting misspelled words [34]; resolving existing coreferences [35]; eliminating redundancy [36] and generating summaries [37].…”

Section: Background and Related Workmentioning

confidence: 99%

A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing

Quimbaya

González

Velandía

et al. 2017

IyU

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Objective: Electronic medical records (EMR) typically contain both structured attributes as well as narrative text. The usefulness of EMR for research and administration is hampered by the difficulty in automatically analyzing their narrative portions. Accordingly, this paper proposes SPIRE, a strategy for prioritizing EMR, using natural language processing in combination with analysis of structured data, in order to identify and rank EMR that match specific queries from clinical researchers and health administrators. Materials and Methods: The resulting software tool was evaluated technically and validated with three cases (heart failure, pulmonary hypertension and diabetes mellitus) compared against expert obtained results. Results and Discussion: Our preliminary results show high sensitivity (70%, 82% and 87% respectively) and specificity (85%, 73.7% and 87.5%) in the resulting set of records. The AUC was between 0.84 and 0.9. Conclusions: SPIRE was successfully implemented and used in the context of a university hospital information system, enabling clinical researchers to obtain prioritized EMR to solve their information needs through collaborative search templates with faster and more accurate results than other existing methods.

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Section: Background and Related Workmentioning

confidence: 99%

A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing

Quimbaya

González

Velandía

et al. 2017

IyU

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“…Parameters such as target lesions, tumor measurements, response, performance status and others need to be agreed upon and embedded in the EMR. While there have been enormous efforts on electronic language capture or natural language processing, these systems cannot extract data that does not exist (despite predictive 'neural' algorithms) nor substitute, in our opinion, for quality, standardized information entered at the point-of-care or alternatively, collected and then entered by data-entry personnel [6][7][8].…”

Section: Data Warehouse and Biorepositorymentioning

confidence: 99%

“…The up front qualification and validation of integral biomarkers to be used in clinical trials will certainly need to be a requirement of any research system that will allocate specific treatment based on these biomarkers [7]. Finally, investigators that amass large amounts of molecular data will have to develop policies regarding their obligation to provide patients results on the incidental finding of mutations, gene signatures and other molecular data that have prognostic or therapeutic implications in cancer or other diseases for that subject or their family.…”

Section: Tcc Clinical and Molecular Database Futurementioning

confidence: 99%

The Creation of an Integrated Health-Information Platform: Building the Framework to Support Personalized Medicine

Sullivan

Hulse

Jacobsen

et al. 2012

Personalized Medicine

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To advance medicine toward better evidence-based, cost-effective and individualized treatment, a new model of discovery, translation and delivery of information must be developed. This requires the collaboration of the major constituents in the areas of healthcare (i.e., patients, clinicians, administrators and researchers) and the partnership of disciplines (i.e., bioinformatics, epidemiologists, statisticians, information technologists, physicians and scientists) and organizations (i.e., drug and device companies, healthcare agencies, academic and community medical centers and information technology firms) to develop an integrative platform. The over-riding goal of this platform is to improve patient care, with the developed system enabling this by providing each of the major constituents with evidence-based and tailored information at the individual patient level.

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“…Both these tools are rule-based, and rely on regular expressions and gazetteers. The TARSQI Toolkit has also been developed for the clinical domain: Med-TTK (Reeves et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Both these tools are rule-based, and rely on regular expressions and gazetteers. The TARSQI Toolkit has also been developed for the clinical domain: Med-TTK (Reeves et al, 2013).In other domains, and for other languages, HeidelTime (Strötgen and Gertz, 2012) and TIMEN (Llorens et al, 2012) are examples of other rulebased systems. These are also developed to be easily extendable to new domains and languages.…”

mentioning

confidence: 99%

Proceedings of BioNLP 2014

2014

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IntroductionThe first day of the BioNLP 2014 workshop continues following the course set by the first ACL workshop on Natural Language Processing in the Biomedical Domain that was held in 2002: BioNLP 2014 provides a venue for exploring challenges and techniques in processing biomedical language and brings together researchers from computational linguistics and biomedical informatics. The submissions to the first day of 2014 workshop organized by SIGBioMed were traditionally very strong and continued demonstrating the considerable breadth of research in biomedical language processing. The 2014 workshop has accepted 12 full and short papers for oral presentations and 7 posters. The first day of the workshop features a keynote that expands the scope of BioNLP beyond its already remarkable breadth The second day of the workshop features a paper submitted to the special track on NLP approaches for assessment of clinical conditions. Kathleen C. Fraser presents the featured talk on using statistical parsing to detect agrammatic aphasia. The track organizers, Thamar Solorio and Yang Liu, serve as discussants.The second day further features an exciting panel that brings together organizers of several shared tasks in biomedical information retrieval and natural language processing. The panel introduces the workshop participants to the long-standing and relatively new community-wide challenges in biomedical and clinical language processing. It also provides an opportunity to discuss the future of the shared tasks in this domain. AcknowledgmentsAs always, we are profoundly grateful to the authors who chose BioNLP as venue for presenting their innovative research. The authors' willingness to share their work through BioNLP consistently makes the workshop noteworthy among the increasing numbers of available venues. We are equally indebted to the program committee members (listed elsewhere in this volume) who produced at least three thorough reviews per paper on a tight review schedule and with an admirable level of insight.iii AbstractZoonotic viruses, viruses that are transmittable between animals and humans, represent emerging or re-emerging pathogens that pose significant public health threats throughout the world. It is therefore crucial to advance current surveillance mechanisms for these viruses through outlets such as phylogeography. Phylogeographic techniques may be applied to trace the origins and geographical distribution of these viruses using sequence and location data, which are often obtained from publicly available databases such as GenBank. Despite the abundance of zoonotic viral sequence data in GenBank records, phylogeographic analysis of these viruses is greatly limited by the lack of adequate geographic metadata. Although more detailed information may often be found in the related articles referenced in these records, manual extraction of this information presents a severe bottleneck. In this work, we propose an automated system for extracting this information using Natural Language Processing (NLP)...

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Detecting temporal expressions in medical narratives

Cited by 29 publications

References 24 publications

A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing

A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing

The Creation of an Integrated Health-Information Platform: Building the Framework to Support Personalized Medicine

Proceedings of BioNLP 2014

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