AIMC Topic: Abbreviations as Topic

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Improving clinical abbreviation sense disambiguation using attention-based Bi-LSTM and hybrid balancing techniques in imbalanced datasets.

Journal of evaluation in clinical practice Jun 21, 2024
RATIONALE: Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to cla...
Abbreviations as Topic Deep Learning Electronic Health Records Natural Language Processing Decision Support Systems, Clinical Humans Neural Networks, Computer
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A deep database of medical abbreviations and acronyms for natural language processing.

Scientific data Jun 2, 2021
The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we pr...
Natural Language Processing Unified Medical Language System Abbreviations as Topic
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The CLASSE GATOR (CLinical Acronym SenSE disambiGuATOR): A Method for predicting acronym sense from neonatal clinical notes.

International journal of medical informatics Feb 14, 2020
OBJECTIVE: To develop an algorithm for identifying acronym 'sense' from clinical notes without requiring a clinically annotated training set.
Pattern Recognition, Automated Medical Subject Headings Abbreviations as Topic Infant, Newborn Algorithms Natural Language Processing Electronic Health Records Humans
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Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
Abbreviation disambiguation in clinical texts is a problem handled well by fully supervised machine learning methods. Acquiring training data, however, is expensive and would be impractical for large numbers of abbreviations in specialized corpora. A...
Electronic Health Records Algorithms Natural Language Processing Abbreviations as Topic Logistic Models Bayes Theorem
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Detection of sentence boundaries and abbreviations in clinical narratives.

BMC medical informatics and decision making Jun 15, 2015
BACKGROUND: In 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, pun...
Medical Records Systems, Computerized Information Storage and Retrieval Abbreviations as Topic Narration Humans Machine Learning Natural Language Processing
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A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.

Applied clinical informatics Jun 3, 2015
OBJECTIVE: To save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language...
User-Computer Interface Time Factors Abbreviations as Topic Health Personnel Natural Language Processing Documentation
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Disambiguation of acronyms in clinical narratives with large language models.

Journal of the American Medical Informatics Association : JAMIA Sep 1, 2024
OBJECTIVE: To assess the performance of large language models (LLMs) for zero-shot disambiguation of acronyms in clinical narratives.
Natural Language Processing Humans Abbreviations as Topic Electronic Health Records Narration Language
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Leveraging PubMed to Create a Specialty-Based Sense Inventory for Spanish Acronym Resolution.

Studies in health technology and informatics Jun 16, 2020
Acronyms frequently occur in clinical text, which makes their identification, disambiguation and resolution an important task in clinical natural language processing. This paper contributes to acronym resolution in Spanish through the creation of a s...
Language Natural Language Processing Artificial Intelligence Abbreviations as Topic PubMed Humans
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A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).

Journal of the American Medical Informatics Association : JAMIA Apr 1, 2017
OBJECTIVE: The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems' capability to handle abbreviations in clin...
Humans Abbreviations as Topic Patient Discharge Electronic Health Records Machine Learning Natural Language Processing
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