ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Modeling and classifying neuronal activity with a fusion of mathematical and machine learning techniques.BMC bioinformatics · 2026Article
- Unstructured Electronic Health Records of Dysphagic Patients Analyzed by Large Language Models.IEEE journal of translational engineering in health and medicine · 2025Article
- Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study.JMIR medical informatics · 2023Article
- Trustworthy assertion classification through prompting.Journal of biomedical informatics · 2022Article
- Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach.PeerJ. Computer science · 2022Article
- A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text.Computers in biology and medicine · 2021Article
- Deep learning in clinical natural language processing: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Active deep learning for the identification of concepts and relations in electroencephalography reports.Journal of biomedical informatics · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Detecting negation in biomedical texts entails the automatic identification of negation cues (e.g. "never", "not", "no longer") as well as the scope of these cues. When medical concepts or terms are identified within the scope of a negation cue, their polarity is inferred as "negative". All the other concepts or words receive a positive polarity. Correctly inferring the polarity is essential for patient cohort retrieval systems, as all inclusion criteria need to be automatically assigned positive polarity, whereas exclusion criteria should receive negative polarity. Motivated by the recent development of techniques using deep learning, we have experimented with a neural negation detection technique and compared it against an existing neural polarity recognition system, which were incorporated in a patient cohort system operating on clinical electroencephalography (EEG) reports. Our experiments indicate that the neural negation detection method produces better patient cohorts then the polarity recognition method.
Indexed as
Identifiers
30815145PMC6371289What OpenQuestion holds
Registered trials
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.