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Article; Bioinformatics

An artificial intelligence approach to early predict symptom-based exacerbations of COPD

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Pages 778-784 | Received 16 Feb 2017, Accepted 03 Feb 2018, Published online: 10 Feb 2018

References

  • Patel J, Burney PG, Newson RB, et al. Global and regional trends in mortality from chronic obstructive pulmonary disease: their relation to poverty, smoking and population change. Eur Respir J. 2014;44(Suppl 58):421.
  • Effing TW, Vercoulen JH, Bourbeau J, et al. Definition of a COPD self-management intervention: International Expert Group consensus. Eur Respir J. 2016;48:46–54.
  • Giorginoll T, et al.. The HOMEY project: a telemedicine service for hypertensive patients personalisation for e-Heaih. In: Floriana G, et al., editors. Proceedings from the 1st International Workshop on Personalisation for e-Health held in conjunction with UM05; 2005 Jul 29; Edinburgh, Scotland. Sheffield (UK): The University of Sheffield; 2005. p. 21–30.
  • Young M, Sparrow D, Gottlieb D, et al. A telephone-linked computer system for COPD care. Chest. 2001;119:1565–1575.
  • Mooney KH, Beck SL, Friedman RH, et al. Telephone-linked care for cancer symptom monitoring: a pilot study. Cancer Pract. 2002;10:147–154.
  • Pinto BM, Friedman R, Marcus BH, et al. Effects of a computer-based, telephone-counseling system on physical activity. Am J Prev Med. 2002;23:113–120.
  • Ramelson HZ, Friedman RH, Ockene JK. An automated telephone-based smoking cessation education and counseling system. Patient Educ Couns. 1999;36:131–144.
  • Zwerink M, Brusse-Keizer M, van der Valk PD, et al. Self management for patients with chronic obstructive pulmonary disease. The Cochrane Library. 2014 [ cited 2018 Jan 31];3: CD002990. DOI: 10.1002/14651858.CD002990.pub3.
  • Sanchez-Morillo D, Fernandez-Granero MA, Leon-Jimenez A. Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: a systematic review. Chron Respir Dis. 2016;13(3):264–283.
  • Vestbo J, Hurd SS, Agustí AG, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med. 2013;187(4):347–365.
  • Mackay AJ, Donaldson GC, Patel AR, et al. Detection and severity grading of COPD exacerbations using the exacerbations of chronic pulmonary disease tool (EXACT). Eur Respir J. 2014;43(3):735–744.
  • Effing TW, Kerstjens HA, Monninkhof EM, et al. Definitions of exacerbations: does it really matter in clinical trials on COPD? Chest. 2009;136:918–923.
  • McKinstry B, Pinnock H, Sheikh A. Telemedicine for management of patients with COPD? The Lancet. 2009;374(9691):672–673.
  • Trappenburg JC, Touwen I, de Weert-van Oene GH, et al. Detecting exacerbations using the clinical COPD questionnaire. Health Qual Life Outcomes. 2010 [cited 2017 Apr 12];8(1):102. DOI: 10.1186/1477-7525-8-102.
  • Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. [ updated 2015.; cited 2016 May 10]. Available from: http://goldcopd.org/.
  • Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A. Computerised analysis of telemonitored respiratory sounds for predicting acute exacerbations of COPD. Sensors. 2015;15(10):26978–26996.
  • Hadjileontiadis LJ. Lung sounds: an advanced signal processing perspective. California (USA): Morgan&Claypool Publishers; 2008.
  • Enderle J, Blanchard S, Bronzino J. Introduction to biomedical engineering. Burlington (USA): Elsevier Academic Press; 2005.
  • Hashemi A, Arabalibiek H, Agin K. Classification of wheeze sounds using wavelets and neural networks. Int Conf Biomed Eng Technol. 2011;11:127–131.
  • Kandaswamy A, Kumar CS, Ramanathan RP, et al. Neural classification of lung sounds using wavelet coefficients. Comput Biol Med. 2004;34:523–537.
  • Yu L, Liu H. Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Fawcett T, Mishra N, editors. Proceedings of the 20th International Conference on Machine Learning (ICML-03); 2003 Aug 21–24; Washington DC (USA). Menlo Park (CA): AAAI Press; 2003. p. 856–863.
  • Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A. Feature selection for high-dimensional data. Berlin, Heidelberg (Germany): Springer; 2015.
  • Rokach L. Ensemble methods in supervised learning. In: Maimon O, Rokach L, editors. Data mining and knowledge discovery handbook. Boston (MA): Springer; 2009. p. 959–979.
  • Sherrod PH. DTREG–Predictive modeling software-manual. [ updated 2014; cited 2016 May 10]. Available from: https://www.dtreg.com/download/.
  • Anthonisen NR, Manfreda J, Warren CP, et al. Antibiotic therapy in exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 1987;106(2):196–204.
  • Fernández-Granero MA, Sánchez-Morillo D, León-Jiménez A, et al. Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms. Biomed Mater Eng. 2014;24:3825–3832.
  • Henderson C, Knapp M, Fernández JL, et al. Cost-effectiveness of telecare for people with social care needs: the whole systems demonstrator cluster randomised trial. Age Ageing. 2014;43(6):794–800.
  • Rixon L, Hirani SP, Cartwright M, et al. A RCT of telehealth for COPD patient's quality of life: the whole system demonstrator evaluation. Clin Respir J. 2015;11:2–25.
  • Azar AT, El-Metwally SM. Decision tree classifiers for automated medical diagnosis. Neural Comput Appl. 2013;23:2387–23403.
  • Sharma N, Om H. Data mining models for predicting oral cancer survivability. Netw Model Anal Health Inform Bioinform. 2013;2(4):285–295.
  • Metz CE. Basic principles of ROC analysis. Semin Nucl Med. 1978;8:283–298.
  • Seemungal TA, Donaldson GC, Bhowmik A, et al. Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2000;161:1608–1613.
  • Langsetmo L, Platt RW, Ernst P, et al. Underreporting exacerbation of chronic obstructive pulmonary disease in a longitudinal cohort. Am J Respir Crit Care Med. 2008;177:396–401.
  • Sanchez-Morillo D, Fernandez-Granero MA, León Jiménez A. Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study. Med Biol Eng Comput. 2015;53:441–451.
  • McKinstry B. The use of remote monitoring technologies in managing chronic obstructive pulmonary disease. QJM Int J Med. 2013;106(10):883–885.
  • Jacome C, Marques A. Computerized respiratory sounds are a reliable marker in subjects with COPD. Respir Care. 2015;60(9):1264–1275.
  • Jensen MH, Cichosz SL, Dinesen B, et al. Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare. J Telemed Telecare. 2012;18(2):99–103.
  • van der Heijden M, Lucas PJF, Lijnse B, et al. An autonomous mobile system for the management of COPD. J Biomed Inform. 2013;46(3):458–469.
  • Burton C, Pinnock H, Mckinstry B. Changes in telemonitored physiological variables and symptoms prior to exacerbations of chronic obstructive pulmonary disease. J Telemed Telecare. 2015;21(1):29–36.
  • Mohktar MS, Redmond SJ, Antoniades NC, et al. Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data. Artif Intell Med. 2015;63(1):51–59.