Predicting frequent COPD exacerbations using primary care data
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TLDR
Patients at risk of exacerbation can be identified from routinely available, computerized primary care data and a robust, clinically based model to predict frequent exacerbation risk was developed.Abstract:
This study was funded by an unrestricted grant from the Respiratory Effectiveness Group (REG; www.effectivenessevaluation.org). Access to data from the Optimum Patient Care Research Database was co-funded by Research in Real-Life Ltd (RiRL, Cambridge, UK). The authors would like to thank Dr John Bukowski of WordsWorld Consulting for editorial assistance drafting this manuscript. Additional editorial support was provided by Elizabeth V Hillyer, DVM, funded by RiRL.read more
Citations
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Journal ArticleDOI
A prospective, observational cohort study of the seasonal dynamics of airway pathogens in the aetiology of exacerbations in COPD
Tom Wilkinson,Emmanuel Aris,Simon Bourne,Simon Bourne,Stuart C. Clarke,Mathieu Peeters,Thierry G. Pascal,Sonia Schoonbroodt,Andrew Tuck,Viktoriya Kim,Kristoffer Ostridge,Karl J. Staples,Nicholas P. Williams,Anthony P. Williams,Stephen A. Wootton,Jeanne-Marie Devaster +15 more
TL;DR: Rises in incidence in winter may be driven by increased pathogen presence as well as an interaction between NTHi airway infection and effects of viral infection.
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Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal
Vanesa Bellou,Lazaros Belbasis,Athanasios Konstantinidis,Ioanna Tzoulaki,Ioanna Tzoulaki,Evangelos Evangelou,Evangelos Evangelou +6 more
TL;DR: A detailed mapping and assessment of the prognostic models for outcome prediction in COPD patients indicates several methodological pitfalls in their development and a low rate of external validation.
Proceedings ArticleDOI
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records
TL;DR: In this paper, a meta-learning framework for clinical risk prediction from longitudinal patient Electronic Health Records (EHR) is proposed, which can be directly used in target risk prediction, and the limited available samples in the target domain can be used for further fine-tuning the model performance.
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A machine learning approach to triaging patients with chronic obstructive pulmonary disease.
Sumanth Swaminathan,Klajdi Qirko,Ted Smith,Ethan Corcoran,Nicholas Wysham,Gaurav Bazaz,George Kappel,Anthony N. Gerber +7 more
TL;DR: A machine learning-based strategy for early detection of exacerbations and subsequent triage and the algorithm is the top performer in sensitivity, specificity, and ppv when predicting a patient’s need for emergency care.
References
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