CogStack

2021

Bendayan, R. et al. (2021) ‘Cognitive Trajectories in Comorbid Dementia With Schizophrenia or Bipolar Disorder: The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register’, The American Journal of Geriatric Psychiatry, 29(6), pp. 604–616. doi:10.1016/j.jagp.2020.10.018.

Bittar, A. et al. (2021) ‘Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis’, JMIR Medical Informatics, 9(4), p. e22397. doi:10.2196/22397.

Carr, E. et al. (2021) ‘Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study’, BMC Medicine, 19(1), p. 23. doi:10.1186/s12916-020-01893-3.

Casey, A. et al. (2021) ‘A systematic review of natural language processing applied to radiology reports’, BMC Medical Informatics and Decision Making, 21(1), p. 179. doi:10.1186/s12911-021-01533-7.

Chilman, N. et al. (2021) ‘Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK’, BMJ Open, 11(3), p. e042274. doi:10.1136/bmjopen-2020-042274.

Coats, T. et al. (2021) ‘An open‐source, expert‐designed decision tree application to support accurate diagnosis of myeloid malignancies’, eJHaem, 2(2), pp. 261–265. doi:10.1002/jha2.182.

Davidson, E.M. et al. (2021) ‘The reporting quality of natural language processing studies: systematic review of studies of radiology reports’, BMC Medical Imaging, 21(1), p. 142. doi:10.1186/s12880-021-00671-8.

Dong, H., Suárez-Paniagua, V., Whiteley, W., et al. (2021) ‘Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation’, Journal of Biomedical Informatics, 116, p. 103728. doi:10.1016/j.jbi.2021.103728.

Dong, H., Suárez-Paniagua, V., Zhang, H., et al. (2021) ‘Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision’, arXiv:2105.01995 [cs] [Preprint]. Available at: http://arxiv.org/abs/2105.01995 (Accessed: 18 December 2021).

Hong, S. et al. (2021) ‘TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer’s disease biomarker levels’, Alzheimer’s & Dementia, 17(10), pp. 1628–1640. doi:10.1002/alz.12330.

Ibrahim, Z.M. et al. (2021) ‘A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data’, arXiv:2011.09361 [cs] [Preprint]. Available at: http://arxiv.org/abs/2011.09361 (Accessed: 18 December 2021).

Irving, J. et al. (2021) ‘Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk’, Schizophrenia Bulletin, 47(2), pp. 405–414. doi:10.1093/schbul/sbaa126.

Kraljevic, Z., Shek, A., et al. (2021) ‘MedGPT: Medical Concept Prediction from Clinical Narratives’, arXiv:2107.03134 [cs] [Preprint]. Available at: http://arxiv.org/abs/2107.03134 (Accessed: 18 December 2021).

Kraljevic, Z., Searle, T., et al. (2021) ‘Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit’, Artificial Intelligence in Medicine, 117, p. 102083. doi:10.1016/j.artmed.2021.102083.

Lau, I.S. et al. (2021) ‘Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life’, BMJ Health & Care Informatics, 28(1), p. e100464. doi:10.1136/bmjhci-2021-100464.

Liu, F. et al. (2021) ‘Self-Alignment Pretraining for Biomedical Entity Representations’, arXiv:2010.11784 [cs] [Preprint]. Available at: http://arxiv.org/abs/2010.11784 (Accessed: 18 December 2021).

Noor, K. et al. (2021) ‘Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals’, arXiv:2108.06835 [cs] [Preprint]. Available at: http://arxiv.org/abs/2108.06835 (Accessed: 18 December 2021).

O’Gallagher, K. et al. (2021) ‘Pre-existing cardiovascular disease rather than cardiovascular risk factors drives mortality in COVID-19’, BMC Cardiovascular Disorders, 21(1), p. 327. doi:10.1186/s12872-021-02137-9.

Oliver, D. et al. (2021) ‘Real-world implementation of precision psychiatry: Transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis’, Schizophrenia Research, 227, pp. 52–60. doi:10.1016/j.schres.2020.05.007.

Patel, H. et al. (2021) ‘Proteomic blood profiling in mild, severe and critical COVID-19 patients’, Scientific Reports, 11(1), p. 6357. doi:10.1038/s41598-021-85877-0.

Ramakrishnan, R. et al. (2021) ‘Accelerometer measured physical activity and the incidence of cardiovascular disease: Evidence from the UK Biobank cohort study’, PLOS Medicine. Edited by A. Paluch, 18(1), p. e1003487. doi:10.1371/journal.pmed.1003487.

Rannikmäe, K. et al. (2021) ‘Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke’, BMC Medical Informatics and Decision Making, 21(1), p. 191. doi:10.1186/s12911-021-01556-0.

Searle, T. et al. (2021) ‘Estimating Redundancy in Clinical Text’, arXiv:2105.11832 [cs] [Preprint]. Available at: http://arxiv.org/abs/2105.11832 (Accessed: 18 December 2021).

Shek, A. et al. (2021) ‘Machine learning‐enabled multitrust audit of stroke comorbidities using natural language processing’, European Journal of Neurology, 28(12), pp. 4090–4097. doi:10.1111/ene.15071.

Slater, L.T., Bradlow, W., Motti, D.FA., et al. (2021) ‘A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text’, Computers in Biology and Medicine, 130, p. 104216. doi:10.1016/j.compbiomed.2021.104216.

Slater, L.T., Bradlow, W., Ball, S., et al. (2021) ‘Improved characterisation of clinical text through ontology-based vocabulary expansion’, Journal of Biomedical Semantics, 12(1), p. 7. doi:10.1186/s13326-021-00241-5.

Slater, L.T., Karwath, A., et al. (2021) ‘Towards similarity-based differential diagnostics for common diseases’, Computers in Biology and Medicine, 133, p. 104360. doi:10.1016/j.compbiomed.2021.104360.

Sykes, D. et al. (2021) ‘Comparison of rule-based and neural network models for negation detection in radiology reports’, Natural Language Engineering, 27(2), pp. 203–224. doi:10.1017/S1351324920000509.

Teo, J.T.H. et al. (2021) ‘Real-time clinician text feeds from electronic health records’, npj Digital Medicine, 4(1), p. 35. doi:10.1038/s41746-021-00406-7.

Viani, N. et al. (2021) ‘A natural language processing approach for identifying temporal disease onset information from mental healthcare text’, Scientific Reports, 11(1), p. 757. doi:10.1038/s41598-020-80457-0.

Wu, H. et al. (2021) ‘Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2’, Journal of the American Medical Informatics Association, 28(4), pp. 791–800. doi:10.1093/jamia/ocaa295.

Zakeri, R. et al. (2021) ‘Biological responses to COVID-19: Insights from physiological and blood biomarker profiles’, Current Research in Translational Medicine, 69(2), p. 103276. doi:10.1016/j.retram.2021.103276.

Zhang, H. et al. (2021) ‘Benchmarking network-based gene prioritization methods for cerebral small vessel disease’, Briefings in Bioinformatics, 22(5), p. bbab006. doi:10.1093/bib/bbab006.

 

2020

Barkhuizen, W. et al. (2020) ‘Community treatment orders and associations with readmission rates and duration of psychiatric hospital admission: a controlled electronic case register study’, BMJ Open, 10(3), p. e035121. doi:10.1136/bmjopen-2019-035121.

Bean, D.M., Al-Chalabi, A., et al. (2020) ‘A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis’, Genes, 11(6), p. 668. doi:10.3390/genes11060668.

Bean, D.M., Kraljevic, Z., et al. (2020) ‘Angiotensin‐converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID‐19 infection in a multi‐site UK acute hospital trust’, European Journal of Heart Failure, 22(6), pp. 967–974. doi:10.1002/ejhf.1924.

Bendayan, R. et al. (2020) ‘Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR’, arXiv:2002.08901 [cs] [Preprint]. Available at: http://arxiv.org/abs/2002.08901 (Accessed: 18 December 2021).

Colling, C. et al. (2020) ‘Predicting high-cost care in a mental health setting’, BJPsych Open, 6(1), p. e10. doi:10.1192/bjo.2019.96.

De Spiegeleer, A. et al. (2020) ‘The Effects of ARBs, ACEis, and Statins on Clinical Outcomes of COVID-19 Infection Among Nursing Home Residents’, Journal of the American Medical Directors Association, 21(7), pp. 909-914.e2. doi:10.1016/j.jamda.2020.06.018.

Fusar-Poli, P. et al. (2020) ‘Real-World Clinical Outcomes Two Years After Transition to Psychosis in Individuals at Clinical High Risk: Electronic Health Record Cohort Study’, Schizophrenia Bulletin, 46(5), pp. 1114–1125. doi:10.1093/schbul/sbaa040.

Gkotsis, G. et al. (2020) ‘Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives’, Dementia and Geriatric Cognitive Disorders, 49(3), pp. 295–302. doi:10.1159/000509123.

Hong, S. et al. (2020) ‘Genome-wide association study of Alzheimer’s disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset’, Translational Psychiatry, 10(1), p. 403. doi:10.1038/s41398-020-01074-z.

Ibrahim, Z., Wu, H. and Dobson, R. (2020) ‘Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units’, arXiv:2004.01431 [cs] [Preprint]. doi:10.3233/FAIA200298.

Ibrahim, Z.M. et al. (2020) ‘On classifying sepsis heterogeneity in the ICU: insight using machine learning’, Journal of the American Medical Informatics Association: JAMIA, 27(3), pp. 437–443. doi:10.1093/jamia/ocz211.

Ive, J. et al. (2020) ‘Generation and evaluation of artificial mental health records for Natural Language Processing’, npj Digital Medicine, 3(1), p. 69. doi:10.1038/s41746-020-0267-x.

Kuang, X. et al. (2020) ‘MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images’, in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, Montreal, QC, Canada: IEEE, pp. 1633–1636. doi:10.1109/EMBC44109.2020.9175987.

Kugathasan, P. et al. (2020) ‘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, pp. 408–415. doi:10.1016/j.schres.2019.10.061.

Martin, T.C. et al. (2020) ‘Dysregulated Antibody, Natural Killer Cell and Immune Mediator Profiles in Autoimmune Thyroid Diseases’, Cells, 9(3), p. 665. doi:10.3390/cells9030665.

McDonald, K. et al. (2020) ‘Prevalence and incidence of clinical outcomes in patients presenting to secondary mental health care with mood instability and sleep disturbance’, European Psychiatry, 63(1), p. e59. doi:10.1192/j.eurpsy.2020.39.

Patel, H. et al. (2020) ‘Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer’s Disease’, Journal of Alzheimer’s Disease, 74(2), pp. 545–561. doi:10.3233/JAD-191163.

Patel, R., Irving, J., et al. (2020) Impact of the COVID-19 pandemic on remote mental healthcare and prescribing in psychiatry. preprint. Psychiatry and Clinical Psychology. doi:10.1101/2020.10.26.20219576.

Patel, R., Smeraldi, F., et al. (2020) Investigating mental and physical disorders associated with COVID-19 in online health forums. preprint. Health Informatics. doi:10.1101/2020.12.14.20248155.

Roubroeks, J.A.Y. et al. (2020) ‘An epigenome-wide association study of Alzheimer’s disease blood highlights robust DNA hypermethylation in the HOXB6 gene’, Neurobiology of Aging, 95, pp. 26–45. doi:10.1016/j.neurobiolaging.2020.06.023.

Searle, T., Ibrahim, Z. and Dobson, R.J. (2020) ‘Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset’, arXiv:2006.07332 [cs, stat] [Preprint]. Available at: http://arxiv.org/abs/2006.07332 (Accessed: 18 December 2021).

Shi, L. et al. (2020) ‘Dickkopf-1 Overexpression in vitro Nominates Candidate Blood Biomarkers Relating to Alzheimer’s Disease Pathology’, Journal of Alzheimer’s disease: JAD, 77(3), pp. 1353–1368. doi:10.3233/JAD-200208.

Slater, L.T., Hoehndorf, R., et al. (2020) Exploring Binary Relations for Ontology Extension and Improved Adaptation to Clinical Text. preprint. Bioinformatics. doi:10.1101/2020.12.04.411751.

Slater, L.T., Bradlow, W., et al. (2020) Komenti: A semantic text mining framework. preprint. Bioinformatics. doi:10.1101/2020.08.04.233049.

Snoeijer, B.T. et al. (2020) ‘Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data’, arXiv:2009.09648 [physics, q-bio] [Preprint]. Available at: http://arxiv.org/abs/2009.09648 (Accessed: 18 December 2021).

Song, X. et al. (2020) ‘Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour’, in Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France: European Language Resources Association, pp. 1303–1310. Available at: https://aclanthology.org/2020.lrec-1.163.

Stewart, C.L., Folarin, A. and Dobson, R. (2020) ‘Personalized acute stress classification from physiological signals with neural processes’, arXiv:2002.04176 [q-bio, stat] [Preprint]. Available at: http://arxiv.org/abs/2002.04176 (Accessed: 18 December 2021).

Sun, S. et al. (2020) ‘Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19’, Journal of Medical Internet Research, 22(9), p. e19992. doi:10.2196/19992.

Teo, J.T. et al. (2020) Impact of ethnicity on outcome of severe COVID-19 infection. Data from an ethnically diverse UK tertiary centre. preprint. Intensive Care and Critical Care Medicine. doi:10.1101/2020.05.02.20078642.

Tissot, H.C. et al. (2020) ‘Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial’, IEEE Journal of Biomedical and Health Informatics, 24(10), pp. 2950–2959. doi:10.1109/JBHI.2020.2977925.

Wang, T. et al. (2020) ‘Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack’, Journal of Visualized Experiments: JoVE [Preprint], (159). doi:10.3791/60794.

Widnall, E. et al. (2020) ‘User Perspectives of Mood-Monitoring Apps Available to Young People: Qualitative Content Analysis’, JMIR mHealth and uHealth, 8(10), p. e18140. doi:10.2196/18140.

Yuan, Y. et al. (2020) ‘Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China’, Engineering, p. S2095809920303581. doi:10.1016/j.eng.2020.10.013.

Zakeri, R. et al. (2020) ‘A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19’, EClinicalMedicine, 28, p. 100574. doi:10.1016/j.eclinm.2020.100574.

Zhang, H. et al. (2020) Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK. preprint. Public and Global Health. doi:10.1101/2020.04.28.20082222.

 

2019

Alex, B. et al. (2019) ‘Text mining brain imaging reports’, Journal of Biomedical Semantics, 10(Suppl 1), p. 23. doi:10.1186/s13326-019-0211-7.

Althubaiti, S. et al. (2019) ‘Ontology-based prediction of cancer driver genes’, Scientific Reports, 9(1), p. 17405. doi:10.1038/s41598-019-53454-1.

Bean, D.M. et al. (2019) ‘Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data’, PLOS ONE. Edited by C. Pizzi, 14(11), p. e0225625. doi:10.1371/journal.pone.0225625.

Boudellioua, I. et al. (2019) ‘DeepPVP: phenotype-based prioritization of causative variants using deep learning’, BMC bioinformatics, 20(1), p. 65. doi:10.1186/s12859-019-2633-8.

Chandran, D. et al. (2019) ‘Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder’, Scientific Reports, 9(1), p. 14146. doi:10.1038/s41598-019-49165-2.

Chua, W. et al. (2019) ‘Data-driven discovery and validation of circulating blood-based biomarkers associated with prevalent atrial fibrillation’, European Heart Journal, 40(16), pp. 1268–1276. doi:10.1093/eurheartj/ehy815.

Fonferko-Shadrach, B. et al. (2019) ‘Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system’, BMJ Open, 9(4), p. e023232. doi:10.1136/bmjopen-2018-023232.

Fusar-Poli, P. et al. (2019) ‘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, p. 109. doi:10.3389/fpsyt.2019.00109.

Gorinski, P.J. et al. (2019) ‘Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches’, arXiv:1903.03985 [cs] [Preprint]. Available at: http://arxiv.org/abs/1903.03985 (Accessed: 18 December 2021).

Kraljevic, Z. et al. (2019) ‘MedCAT — Medical Concept Annotation Tool’, arXiv:1912.10166 [cs, stat] [Preprint]. Available at: http://arxiv.org/abs/1912.10166 (Accessed: 18 December 2021).

Patel, R. et al. (2019) ‘F55. CLINICAL OUTCOMES ASSOCIATED WITH ILLICIT SUBSTANCE USE IN FIRST EPISODE PSYCHOSIS (FEP): A TEXT MINING STUDY OF ELECTRONIC HEALTH RECORDS’, Schizophrenia Bulletin, 45(Supplement_2), pp. S276–S276. doi:10.1093/schbul/sbz018.467.

Ranjan, Y. et al. (2019) ‘RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices’, JMIR mHealth and uHealth, 7(8), p. e11734. doi:10.2196/11734.

Searle, T. et al. (2019) ‘MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation’, arXiv:1907.07322 [cs] [Preprint]. Available at: http://arxiv.org/abs/1907.07322 (Accessed: 18 December 2021).

Shah, A.D. et al. (2019) ‘Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death’, Journal of Biomedical Semantics, 10(Suppl 1), p. 20. doi:10.1186/s13326-019-0214-4.

Tissot, H. et al. (2019) Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-automated Simulation Based on the LeoPARDS Trial. preprint. Health Informatics. doi:10.1101/19005603.

Tissot, H. and Dobson, R. (2019) ‘Combining string and phonetic similarity matching to identify misspelt names of drugs in medical records written in Portuguese’, Journal of Biomedical Semantics, 10(Suppl 1), p. 17. doi:10.1186/s13326-019-0216-2.

Wheater, E. et al. (2019) ‘A validated natural language processing algorithm for brain imaging phenotypes from radiology reports in UK electronic health records’, BMC medical informatics and decision making, 19(1), p. 184. doi:10.1186/s12911-019-0908-7.

Wu, H. et al. (2019) ‘Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach’, JMIR medical informatics, 7(4), p. e14782. doi:10.2196/14782.

 

2018

Jackson, R. et al. (2018) ‘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), p. 47. doi:10.1186/s12911-018-0623-9.

Wu, H. et al. (2018) ‘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), pp. 530–537. doi:10.1093/jamia/ocx160.

 

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