ArticlePloS one2019
Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 4 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 4 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- The reporting quality of natural language processing studies: systematic review of studies of radiology reports.BMC medical imaging · 2021Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Language dysfunction as a primary feature of cognitive decline in neurological populations.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Review
- Real-time data in cancer registries: Validation of an automated data extraction system.iScience · 2025Article
- Enhancing predictive analytics in mandibular third molar extraction using artificial intelligence: A CBCT-Based study.The Saudi dental journal · 2024Article
- TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.BMC medical informatics and decision making · 2024Article
- Development of a natural language processing algorithm for the detection of spinal metastasis based on magnetic resonance imaging reports.North American Spine Society journal · 2024Article
- AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical.AJNR. American journal of neuroradiology · 2024Article
- Artificial intelligence applied in acute ischemic stroke: from child to elderly.La Radiologia medica · 2024Review
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
- Early Disease Prediction Using a Text-Numerical Hybrid Model Using Large-Scale Clinical Real-World Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Optimization of Radiology Diagnostic Services for Patients with Stroke in Multidisciplinary Hospitals.Materia socio-medica · 2024Review
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review.JMIR medical informatics · 2023Article
- Article
- Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models.Diagnostics (Basel, Switzerland) · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
background and purposeThis project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes. MATERIALS AND
methodsAll brain MRI reports from a single academic institution over a two year period were randomly divided into 2 groups for ML: training (70%) and testing (30%). Using "quanteda" NLP package, all text data were parsed into tokens to create the data frequency matrix. Ten-fold cross-validation was applied for bias correction of the training set. Labeling for AIS was performed manually, identifying clinical notes. We applied binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine for the binary classifiers, and we assessed performance of the algorithms by F1-measure. We also assessed how n-grams or term frequency-inverse document frequency weighting affected the performance of the algorithms.
resultsOf all 3,204 brain MRI documents, 432 (14.3%) were labeled as AIS. AIS documents were longer in character length than those of non-AIS (median [interquartile range]; 551 [377-681] vs. 309 [164-396]). Of all ML algorithms, single decision tree had the highest F1-measure (93.2) and accuracy (98.0%). Adding bigrams to the ML model improved F1-mesaure of naïve Bayesian classification, but not in others, and term frequency-inverse document frequency weighting to data frequency matrix did not show any additional performance improvements.
conclusionsSupervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. Single decision tree was the best classifier to identify brain MRI reports with AIS.
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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.