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Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports

https://doi.org/10.1371/journal.pone.0234908
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Random Forests
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Natural Language–based Machine Learning Models for the Annotation of Clinical Radiology Reports
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Validity of Hospital Discharge Diagnosis Codes for Stroke
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Long Short-Term Memory
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Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke
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Recurrent Convolutional Neural Networks for Text Classification
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Bag of Tricks for Efficient Text Classification
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach
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Neural Network Methods for Natural Language Processing
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Classification and Regression Trees.
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AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
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Efficient Estimation of Word Representations in Vector Space
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Linking GloVe with word2vec
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Comparative Study of CNN and RNN for Natural Language Processing
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Later Midline Shift Is Associated with Better Outcomes after Large Middle Cerebral Artery Stroke
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Additional file 1 of A systematic review of natural language processing applied to radiology reports
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Additional file 2 of A systematic review of natural language processing applied to radiology reports
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Additional file 1 of Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT)
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