Summarize this content to 100 words: Chalmers, J. D., Chang, A. B., Chotirmall, S. H., Dhar, R. & McShane, P. J. Bronchiectasis Nat. Rev. Dis. Primers 4, 45 (2018).
Google Scholar
Martínez-García, M. Á. et al. Multidimensional approach to non-cystic fibrosis bronchiectasis: the FACED score. Eur. Respir J. 43, 1357–1367 (2014).
Google Scholar
Chalmers, J. D. et al. The bronchiectasis severity index. An international derivation and validation study. Am. J. Respir Crit. Care Med. 189, 576–585 (2014).
Google Scholar
Yang, B. et al. The disease burden of bronchiectasis in comparison with chronic obstructive pulmonary disease: a National database study in Korea. Ann. Transl Med. 7, 770 (2019).
Google Scholar
Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).
Google Scholar
Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N Engl. J. Med. 380, 1347–1358 (2019).
Google Scholar
Chalmers, J. D. et al. Characterization of the frequent exacerbator phenotype in bronchiectasis. Am. J. Respir Crit. Care Med. 197, 1410–1420 (2018).
Google Scholar
Choi, H., McShane, P. J., Aliberti, S. & Chalmers, J. D. Bronchiectasis management in adults: state of the Art and future directions. Eur. Respir J. 63, 2301519 (2024).
Google Scholar
Hecht-Nielsen, R. Elsevier,. Theory of the backpropagation neural network. In Neural Networks for Perception, 65–93 (1992).Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).
Google Scholar
Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Understanding deep learning (still) requires rethinking generalization. Commun. ACM. 64, 107–115 (2021).
Google Scholar
Hawkins, D. M. The problem of overfitting. J. Chem. Inf. Comput. Sci. 44, 1–12 (2004).
Google Scholar
Lee, H. et al. KMBARC registry: protocol for a multicentre observational cohort study on non-cystic fibrosis bronchiectasis in Korea. BMJ Open. 10, e034090 (2020).
Google Scholar
Lee, H. et al. Characteristics of bronchiectasis in korea: first data from the Korean multicentre bronchiectasis audit and research collaboration registry and comparison with other international registries. Respirology 26, 619–621 (2021).
Google Scholar
Bestall, J. et al. Usefulness of the medical research Council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary disease. Thorax 54, 581–586 (1999).
Google Scholar
Kim, H. K. et al. Validation of the Korean version of the bronchiectasis health questionnaire. Tuberc Respir Dis. 83, 228–234 (2020).
Google Scholar
Spinou, A. et al. The development and validation of the bronchiectasis health questionnaire. Eur. Respir J. 49, 1601532 (2017).
Google Scholar
Miller, M. R. et al. Standardisation of spirometry. Eur. Respir J. 26, 319–338 (2005).
Google Scholar
Choi, J. K., Paek, D. & Lee, J. O. Normal predictive values of spirometry in Korean population. Tuberc Respir Dis. 58, 230–242 (2005).
Google Scholar
Murray, M., Pentland, J., Turnbull, K., MacQuarrie, S. & Hill, A. Sputum colour: a useful clinical tool in non-cystic fibrosis bronchiectasis. Eur. Respir J. 34, 361–364 (2009).
Google Scholar
Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing; twenty-fifth informational supplement. CLSI document M100-S25 (Clinical and Laboratory Standards Institute, Wayne, PA, 2015).Murray, P. & Washington, J. Microscopic and Bateriologic analysis of expectorated sputum. Mayo Clin. Proc. 50, 339–344 (1975).
Google Scholar
Griffith, D. E. et al. An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases. Am. J. Respir Crit. Care Med. 175, 367–416 (2007).
Google Scholar
Reiff, D. B., Wells, A. U., Carr, D. H., Cole, P. & Hansell, D. CT findings in bronchiectasis: limited value in distinguishing between idiopathic and specific types. Am. J. Roentgenol. 165, 261–267 (1995).
Google Scholar
Pasteur, M. C., Bilton, D. & Hill, A. T. British thoracic society guideline for non-CF bronchiectasis. Thorax 65, i1–i58 (2010).
Google Scholar
Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. In Proc. Eur. Conf. Comput. Learn. Theory, 23–37 (Springer, 1997).
Source link
AI based prediction of severe exacerbation in Asian bronchiectasis patients using the KMBARC registry
Leave a Comment