CogStack

2021

Carr, Ewan et al. “Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.” BMC Medicine (2021). https://doi.org/10.1186/s12916-020-01893-3

Coats, Thomas et al. “An open‐source, expert‐designed decision tree application to support accurate diagnosis of myeloid malignancies.” eJHaem (2021). https://doi.org/10.1002/jha2.182

Kraljevic, Zeljko et al. “MedGPT: Medical Concept Prediction from Clinical Narratives.” arXiv preprint arXiv:2107.03134 (2021)

Kraljevic, Zeljko et al. “Multi-domain clinical natural language processing with MedCAT: the Medical Concept Annotation Toolkit.” Artificial Intelligence in Medicine 117 (2021): 102083. https://doi.org/10.1016/j.artmed.2021.102083

Lau, Ivan Shun et al. “Natural Language Word-Embeddings as a glimpse into healthcare at the End Of Life.” medRxiv (2021)

O’Gallagher, Kevin et al. “Pre-existing cardiovascular disease rather than cardiovascular risk factors drives mortality in COVID-19.” BMC Cardiovascular Disorders 21, no. 1 (2021): 1-13. https://doi.org/10.1186/s12872-021-02137-9

Oliver, Dominic et al. “Real-world implementation of precision psychiatry: transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis.” Schizophrenia research 227 (2021): 52-60. https://doi.org/10.1016/j.schres.2020.05.007

Noor, Kawsar et al. “Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals.” arXiv preprint arXiv:2108.06835 (2021)

Searle, Thomas et al. “Estimating Redundancy in Clinical Text.” arXiv preprint arXiv:2105.11832 (2021)

Shek, Anthony et al. “Machine learning enabled multi‐Trust audit of stroke co‐morbidities using Natural language Processing.” European Journal of Neurology (2021). https://doi.org/10.1111/ene.15071

Teo, James et al. “Real-time clinician text feeds from electronic health records” Nature Digital Medicine (2021) 20205617. https://doi.org/10.1101/2020.10.02.20205617

Wu, Honghan et al. “Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2” JAMIA (2021). https://doi.org/10.1093/jamia/ocaa295

Zakeri, Rosita et al. “Biological responses to COVID-19: Insights from physiological and blood biomarkers” Current Research in Translational Medicine (2021). https://doi.org/10.1016/j.retram.2021.103276

2020

Bean, Daniel M et al. “ACE-inhibitors and Angiotensin-2 Receptor Blockers are not associated with severe SARS-COVID19 infection in a multi-site UK acute Hospital Trust.” European Journal of Heart Failure 22, no. 6 (2020): 967-974. https://doi.org/10.1101/2020.04.07.20056788 

Bendayan, Rebecca et al. “Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR” arXiv (2020) arXiv:2002.08901. https://arxiv.org/pdf/2002.08901.pdf

Coats, Thomas; et al. “Decision tree application (DTA) to support accurate diagnosis of myeloid malignancies.” British Journal of Haematology 189 (2020), pp. 10-11

Gkotsis, George et al. “Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives.” Dementia and Geriatric Cognitive Disorders 49, no. 3 (2020): 295-302. https://doi.org/10.1159/000509123

Ibrahim, Zina M et al. “A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data.” arXiv preprint arXiv:2011.09361 (2020)

Kugathasan, Pirathiv et al. “Association of physical health multimorbidity with mortality in people with schizophrenia spectrum disorders: Using a novel semantic search system that captures physical diseases in electronic patient records.” Schizophrenia research 216 (2020): 408-415. https://doi.org/10.1016/j.schres.2019.10.061

Searle, Thomas et al. “Experimental evaluation and development of a silver-standard for the MIMIC-III clinical coding dataset.” arXiv preprint arXiv:2006.07332 (2020)

Tissot, Hegler et al. Natural language processing for mimicking clinical trial recruitment in critical care: a semi-automated simulation based on the LeoPARDS triaI. IEEE journal of biomedical and health informatics 24, no. 10 (2020): 2950-2959.

Wang, Tao et al. “Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack” Jove Journal (2020). https://dx.doi.org/10.3791/60794

2019

Bean, Daniel M., et al. “Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data.” PloS one 14.11 (2019). https://doi.org/10.1371/journal.pone.0225625

Fusar-Poli, Paolo et al. “Real world implementation of a transdiagnostic risk calculator for the automatic detection of individuals at risk of psychosis in clinical routine: study protocol.” Frontiers in psychiatry 10 (2019): 109. https://doi.org/10.3389/fpsyt.2019.00109

Kraljevic, Zeljko, et al. “MedCAT–Medical Concept Annotation Tool.” arXiv preprint arXiv:1912.10166 (2019). https://arxiv.org/abs/1912.10166

Searle, Thomas, et al. “MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation.” arXiv preprint arXiv:1907.07322 (2019). https://arxiv.org/abs/1907.07322

Tissot, Hegler, et al. “Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-automated Simulation Based on the LeoPARDS Trial.” medRxiv (2019): 19005603. https://doi.org/10.1101/19005603

 

2018

Jackson, Richard, et al. “CogStack-experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital.” BMC medical informatics and decision making 18.1 (2018): 47. https://doi.org/10.1186/s12911-018-0623-9

Wu, Honghan, et al. “SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research.” Journal of the American Medical Informatics Association 25.5 (2018): 530-537. https://doi.org/10.1093/jamia/ocx160

2017

Wu, Honghan, et al. “SemEHR: surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research.” The Lancet 390 (2017): S97. https://doi.org/10.1016/S0140-6736(17)33032-5

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