ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition.
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 18 papers.
What it found
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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
18 citing papers in PubMed, 26 citations in OpenAlex.
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- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation Using Combined Contextual Embeddings.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Applications of artificial intelligence in drug development using real-world data.Drug discovery today · 2021Review
- A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records.IEEE access : practical innovations, open solutions · 2021Article
- Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models.JMIR medical informatics · 2020Article
- Clinical concept extraction using transformers.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.Applied soft computing · 2020Article
- Deep learning in clinical natural language processing: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining.Journal of healthcare engineering · 2020Article
- Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- A study of deep learning methods for de-identification of clinical notes in cross-institute settings.BMC medical informatics and decision making · 2019Article
- Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study.JMIR medical informatics · 2019Article
- A Study of Deep Learning Methods for De-identification of Clinical Notes at Cross Institute Settings.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
There has been an increasing interest in developing deep learning methods to recognize clinical concepts from narrative clinical text. Recently, several studies have reported that Recurrent Neural Networks (RNNs) outperformed traditional machine learning methods such as Conditional Random Fields (CRFs). Deep learning-based Named Entity Recognition (NER) systems often use statistical language models to learn word embeddings from unlabeled corpora. However, current word embedding methods have limitations to learn decent representations for low-frequency words. Medicine is a knowledge-extensive domain; existing medical knowledge has the potential to improve feature representations for less frequent yet important words. However, it is still not clear how existing medical knowledge can help deep learning models in clinical NER tasks. In this study, we integrated medical knowledge from the Unified Medical Language System with word embeddings trained from an unlabeled clinical corpus in RNNs for detection of problems, treatments and lab tests. We examined three different ways to generate medical knowledge features, including a dictionary lookup program, the KnowledgeMap system, and the MedLEE system. We also compared representing medical knowledge as one-hot vectors versus representing medical knowledge as embedding layers. The evaluation results showed that the RNN with medical knowledge as embedding layers achieved new state-of-the-art performance (a strict F1 score of 86.21% and a relaxed F1 score of 92.80%) on the 2010 i2b2 corpus, outperforming an RNN with only word embeddings and RNNs with medical knowledge as one-hot vectors. This study demonstrated an efficient way of integrating medical knowledge with distributed word representations for clinical NER.
Indexed as
Identifiers
30815153PMC6371322W2940685834What 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.