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This paper presents a system supporting clinical decisions for patients with Chronic Obstructive Pulmonary Disease (COPD). The system should partially fill the gaps highlighted during an analysis of the current state of the art of Clinical Decision Support Systems (CDSS) for telemonitoring patients affected by COPD. The first step taken was to replicate the performance of similar decision support systems found in the scientific literature. Using physiological parameters drawn from respiratory function tests on 414 patients, two predictive models were created using two machine-learning algorithms: neural network and support vector machine. Performance was comparable to that described in the literature. The results made it possible to affirm that the data available were sufficient to evaluate the extent of respiratory deficit. The next step was to create a new predictive model with better performance than previously obtained. The C5.0 Machine Learning Algorithm was chosen for the development of the model. The resulting performance on the data available was significantly better than with the two previous models. This new predictive model, called COPD, was then implemented in a user interface created using Java programming language. The new software developed, which enables the evaluation and classification of respiratory test results and which can be used in many clinical applications, provides excellent performance compared to the current state of the art.
Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380:2095–128.
Lopez-Campos JL, Ruiz-Ramos M, Soriano JB. Mortality trends in chronic obstructive pulmonary disease in Europe, 1994-2010: a joinpoint regression analysis. Lancet Respir Med. 2014;2:54–62. CrossRef
Vos T, Allen C, Arora M, Barber RM, Bhutta ZA, Brown A, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the global burden of disease study 2015. Lancet. 2016;388(10053):8–14. https://doi.org/10.1016/S0140-6736(16)31678-6.
Guarascio SM, Ray SM, Finch CK, Self TH. The clinical and economic burden of chronic obstructive pulmonary disease in the USA. Clinicoecon and Outcomes Res. 2013;2015(5):235–45. https://doi.org/10.2147/CEOR.S34321.
Pinnock H, Hanley J, McCloughan L, Todd A, Krishan A, Lewis S, et al. Effectiveness of telemonitoring integrated into existing clinical services on hospital admission for exacerbation of chronic obstructive pulmonary disease: researcher blind, multicenter, randomized controlled trial. BMJ. 2013;347:f6070. https://doi.org/10.1136/bmj.f6070. CrossRef
Fernández-Granero MA, Sánchez-Morillo D, León-Jiménez A, Crespo LF. Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms. Bio-Med Mater Eng. 2014;24(6):3825–32. https://doi.org/10.3233/BME-141212.
Hardinge M, Rutter H, Velardo C, Shah SA, Williams V, Tarassenko L, et al. Using a mobile health application to support self-management in chronic obstructive pulmonary disease: a six-month cohort study. BMC Medical Informatics and Decision Making. 2015;15(46):46. https://doi.org/10.1186/s12911-015-0171-5. CrossRef
Mohktar MS, Redmond SJ, Antoniades NC, Rochford PD, Pretto JJ, Basilakis J, 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):51–9. https://doi.org/10.1016/j.artmed.2014.12.003. CrossRef
Mudura VA, Frosini F, Iadanza E. Clinical decision support systems for COPD: a general overview. In: Eskola H, Väisänen O, Viik J, Hyttinen J, editors. EMBEC & NBC 2017. EMBEC 2017, NBC 2017. IFMBE proceedings, vol. 65. Singapore: Springer; 2018. https://doi.org/10.1007/978-981-10-5122-7_234.
IBM: IBM SPSS Modeler [online]. Available: https://www.ibm.com/it-it/marketplace/spss-modeler. Accessed on 28 Aug 2018.
Karakis R, Guler I, Isik AH. Feature selection in pulmonary function test data with machine learning methods. In: Proc. 2013 21st signal processing and communications applications conference (SIU), Haspolat; 2013. p. 1–4. https://doi.org/10.1109/SIU.2013.6531578.
Guidi G, Iadanza E, Pettenati MC, Milli M, Pavone F, Biffi Gentili G. Heart failure artificial intelligence-based computer aided diagnosis telecare system. In: Donnelly M, Paggetti C, Nugent C, Mokhtari M, editors. Impact analysis of solutions for chronic disease prevention and management. ICOST 2012. Lecture notes in computer science, vol. 7251. Berlin: Springer; 2012. https://doi.org/10.1007/978-3-642-30779-9_44.
Guidi G, Pettenati MC, Miniati R, Iadanza E. Random forest for automatic assessment of heart failure severity in a telemonitoring scenario. In: Proc. 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Osaka; 2013. p. 3230–3. https://doi.org/10.1109/EMBC.2013.6610229.
Guidi G, Pettenati MC, Miniati R, Iadanza E. Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies. In: Proc. 2012 annual international conference of the IEEE engineering in medicine and biology society, San Diego, CA; 2012. p. 2210–3. https://doi.org/10.1109/EMBC.2012.6346401.
Guidi G, Melillo P, Pettenati MC, Milli M, Iadanza E. Performance assessment of a clinical decision support system for analysis of heart failure. In: Roa RL, editor. XIII Mediterranean conference on medical and biological engineering and computing 2013. IFMBE proceedings, vol. 41. Cham: Springer; 2014. https://doi.org/10.1007/978-3-319-00846-2_335.
Iadanza E, Mudura VA. A decision support system for chronic obstructive pulmonary disease (COPD). In: Lhotska L, Sukupova L, Lacković I, Ibbott G, editors. World congress on medical physics and biomedical engineering 2018. IFMBE proceedings, vol 68/3. Singapore: Springer; 2019. https://doi.org/10.1007/978-981-10-9023-3_57.
- An automatic system supporting clinical decision for chronic obstructive pulmonary disease
- Springer Berlin Heidelberg
Health and Technology
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