[HTML][HTML] Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing

M Fernandes, MB Westover, AB Singhal, SF Zafar - medRxiv, 2024 - ncbi.nlm.nih.gov
BACKGROUND: Multi-center electronic health records (EHR) can support quality
improvement initiatives and comparative effectiveness research in stroke care. However …

Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing

M Bento Fernandes, B Westover, AB Singhal, SF Zafar - medRxiv, 2024 - medrxiv.org
BACKGROUND: Multi-center electronic health records (EHR) can support quality
improvement initiatives and comparative effectiveness research in stroke care. However …

Assessing stroke severity using electronic health record data: a machine learning approach

E Kogan, K Twyman, J Heap, D Milentijevic… - BMC medical informatics …, 2020 - Springer
Background Stroke severity is an important predictor of patient outcomes and is commonly
measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these …

P287 Use of machine learning to determine stroke severity of patients diagnosed with stroke in claims data

E Kogan, K Twyman, J Heap, D Milentijevic… - European Heart …, 2018 - academic.oup.com
Background: The National Institutes of Health Stroke Scale (NIHSS) scores are often
recorded as free text in the neurologist's diagnosis reports, and not readily available in …

Use of machine learning to determine stroke severity of patients diagnosed with stroke in integrated claims-medical records dataset

E Kogan, K Twyman, J Heap, D Milentijevic, JH Lin… - Circulation, 2017 - Am Heart Assoc
Introduction: The National Institutes of Health Stroke Scale (NIHSS) scores are often
recorded in a form of free text in the neurologist's diagnosis reports and this information is …

[HTML][HTML] Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language …

Z Gu, X He, P Yu, W Jia, X Yang, G Peng, P Hu… - Artificial intelligence in …, 2024 - Elsevier
Background: Stroke is a prevalent disease with a significant global impact. Effective
assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and …

[HTML][HTML] Identifying stroke-related quantified evidence from electronic health records in real-world studies

L Yang, X Huang, J Wang, X Yang, L Ding, Z Li… - Artificial Intelligence in …, 2023 - Elsevier
Background Stroke is one of the leading causes of death and disability worldwide. The
National Institutes of Health Stroke Scale (NIHSS) scores in electronic health records …

Abstract TP307: Validation of a Machine Learning Approach to Determine Stroke Severity of Patients Diagnosed With Stroke in Claims Data

E Kogan, E Sjoeland, D Milentijevic, JH Lin, M Alberts - Stroke, 2020 - Am Heart Assoc
Introduction: The National Institutes of Health Stroke Scale (NIHSS) scores are often not
readily available in structured claims databases. We have previously demonstrated that a …

[HTML][HTML] Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and …

E Hsu, AT Bako, T Potter, AP Pan, GW Britz, J Tannous… - JMIR AI, 2023 - ai.jmir.org
Background Neuroimaging is the gold-standard diagnostic modality for all patients
suspected of stroke. However, the unstructured nature of imaging reports remains a major …

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records

HJ Lee, LH Schwamm, LH Sansing, H Kamel… - NPJ Digital …, 2024 - nature.com
Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke
prevention efforts but can be diagnostically challenging. We trained and validated an …