ArticleBMC medical informatics and decision making2015
Detection of sentence boundaries and abbreviations in clinical narratives.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current approaches to identify sections within clinical narratives from electronic health records: a systematic review.BMC medical research methodology · 2019Pooled it
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- Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan.PLOS digital health · 2022Article
- Estimating Sentence-like Structure in Synthetic Languages Using Information Topology.Entropy (Basel, Switzerland) · 2022Article
- Hybrid Ensemble-Rule Algorithm for Improved MEDLINE® Sentence Boundary Detection.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- EHR problem list clustering for improved topic-space navigation.BMC medical informatics and decision making · 2019Article
- A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems.Journal of healthcare engineering · 2019Article
- Deep Neural Architectures for Discourse Segmentation in E-Mail Based Behavioral Interventions.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Clinical Natural Language Processing in languages other than English: opportunities and challenges.Journal of biomedical semantics · 2018Review
- Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records.Journal of healthcare engineering · 2017Article
- A Quantitative and Qualitative Evaluation of Sentence Boundary Detection for the Clinical Domain.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- Fine-grained information extraction from German transthoracic echocardiography reports.BMC medical informatics and decision making · 2015Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIn Western languages the period character is highly ambiguous, due to its double role as sentence delimiter and abbreviation marker. This is particularly relevant in clinical free-texts characterized by numerous anomalies in spelling, punctuation, vocabulary and with a high frequency of short forms.
methodsThe problem is addressed by two binary classifiers for abbreviation and sentence detection. A support vector machine exploiting a linear kernel is trained on different combinations of feature sets for each classification task. Feature relevance ranking is applied to investigate which features are important for the particular task. The methods are applied to German language texts from a medical record system, authored by specialized physicians.
resultsTwo collections of 3,024 text snippets were annotated regarding the role of period characters for training and testing. Cohen's kappa resulted in 0.98. For abbreviation and sentence boundary detection we can report an unweighted micro-averaged F-measure using a 10-fold cross validation of 0.97 for the training set. For test set based evaluation we obtained an unweighted micro-averaged F-measure of 0.95 for abbreviation detection and 0.94 for sentence delineation. Language-dependent resources and rules were found to have less impact on abbreviation detection than on sentence delineation.
conclusionsSentence detection is an important task, which should be performed at the beginning of a text processing pipeline. For the text genre under scrutiny we showed that support vector machines exploiting a linear kernel produce state of the art results for sentence boundary detection. The results are comparable with other sentence boundary detection methods applied to English clinical texts. We identified abbreviation detection as a supportive task for sentence delineation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.