Artificial Intelligence for Long-term Respiratory Disease Management

This paper presents the strengths, weaknesses, opportunities, and threats for Artificial Intelligence (AI) as applied to long-term respiratory disease management. This analysis will help to identify, understand, and evaluate key aspects of the technology as well as the various internal/external forces which influence its success in this application space. Such understanding is instrumental to ensure judicial planning and implementation with suitable safeguards being considered. AI has the potential to radically change how respiratory disease management is conducted and may help clinicians to realise new treatment paradigms. The application of AI is clearly not specific to respiratory disease management; however it is a chronic disease that requires on-going monitoring and well evidenced decision making regarding treatment pathways or medication modification. This work emphasises the current position of AI as applied to respiratory disease management and identifies the issues to help develop strategic directions to ensure successful implementation, evidenced by ubiquitous acceptance and uptake.

Content

Author and article information

Contributors

Philip A. Catherwood

Joseph Rafferty

James McLaughlin

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-5

Affiliations

[0001]School of Engineering

Ulster University

Shore Rd., N’abbey

Co.Antrim, BT370QB

[0002]Sch. of Computing & Mathematics

Ulster University

Shore Rd., N’abbey

Co.Antrim, BT370QB

Article

DOI: 10.14236/ewic/HCI2018.65

SO-VID: b801a49f-5f92-4c33-86e2-1bb7149bdf03

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

History

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.65

Self URI (journal page): https://ewic.bcs.org/

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Comments

Comment on this article

[1] L. YuH. ChenQ. DouJ. QinP.A. Heng 2017 Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks IEEE T. Med. Im. 36 994 1004

[2] P. JagadevH.G. Virani 2017 Detection of leukemia and its types using image processing and machine learning Intl. Conf. Trends Elect. Info. (ICEI), Tirunelveli 522 526 IEEE USA

[3] F. Celesti 2017 Big data analytics in genomics: The point on Deep Learning solutions IEEE Symp. Comp. Comms. (ISCC), Heraklion 306 309 IEEE USA

[4] W. XiangJ. YinG. Lim 2015 A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints Art. Int. in Med. 63 91 106

[5] N. DasM. TopalovicW. Janssens 2018 Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential Curr. Opin. Pulm. Med 24 117 123

[6] IEEE Spectrum News 2017 AI versus doctors spectrum.ieee.org/static/ai-vs-doctors 6 3 18

[7] British Lung Foundation 2018 Lung disease in the UK http://statistics.blf.org.uk/lung-disease-uk-big-picture 5 6 18

[8] WHO 2018 Chronic respiratory diseases http://www.who.int/respiratory/en/ 6 3 18

[9] J. Patel 2014 Global and regional trends in mortality from chronic obstructive pulmonary disease: their relation to poverty, smoking and population change Eur. Respir. Journal 44 1239 1247

[10] H.D. Komarow 2011 Impulse oscillometry in the evaluation of diseases of the airways in children Ann. Allergy Asthma Immunol 106 191 199

[11] K. Khan 2017 Potential biomarkers for the diagnosis of respiratory tract infection and lungs cancer Cell Molecular Biology 30 46 52

[12] H.J. KimM.G. ChoiM.K. ParkY.S. Seo 2017 Predictive and Prognostic Biomarkers of Respiratory Diseases due to Particulate Matter Exposure J. Cancer Prev 22 6 15

[13] Z. Mo 2018 Acute effects of air pollution on respiratory disease mortalities and outpatients in Southeastern China Nature 8 1 9 Springer USA

[14] M.E. Giannaccini 2017 Respiratory simulator for robotic respiratory tract treatments IEEE Intl. Conf. Robotics and Biomimetics Macau, Macao 5 8 Dec 2017 2314 2319 IEEE, USA

[15] H. RehoumaR. NoumeirP. JouvetW. BouachirS. Essouri 2017 A computer vision method for respiratory monitoring in intensive care environment using RGB-D cameras 7th Intl. Conf. Im. Proc. Theory, Tools and App., Montreal, QC 28 Nov. 1 Dec. 2017 1 6 IEEE USA

[16] S. Nousias 2018 An mHealth System for Monitoring Medication Adherence in Obstructive Respiratory Diseases Using Content Based Audio Classification IEEE Access 6 11871 11882

[17] E. Newton-Rex 2017 Business Insider http://uk.businessinsider.com/artificial-intelligence-ai-most-impressive-achievements-2017-3?r=US&IR=T/#what-ai-cando-everyday-humanstuff-1 24 3 18

[18] A.J. GuarascioS.M. RayC.K. Finch 2013 The clinical and economic burden of chronic obstructive pulmonary disease in the USA Clinicoecon Outcomes Res 5 235 245

[19] M.A. Fernandez-GraneroD. Sanchez-MorilloA. Leon-Jimenez 2018 An artificial intelligence approach to early predict symptom-based exacerbations of COPD Biotech. & Biotech. Equipment 10.1080/13102818.2018.1437568

[20] C. Esteban 2015 Machine learning for COPD exacerbation prediction Eur. Resp. J. 46 OA3282

[21] W. Knight 2017 MIT www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/ 3 3 18

[22] D. Gershgorn 2017 Quartz qz.com/1146753/ai-is-now-so-complex-its-creators-cant-trust-why-it-makes-decisions/ 3 3 18

[23] K. Wetzel 2018 Digital Trends www.digitaltrends.com/home/smart-home-technology-trends-and-whats-ahead/ 21 3 18

[24] Ofcom 2017 Ofcom www.ofcom.org.uk/about-ofcom/latest/media/media-releases/2017/northern-ireland-broadband 21 3 18

[25] J. PickupM. HollowayK. Samsi 2015 Real-time continuous glucose monitoring in type 1 diabetes: a qualitative framework analysis of patient narratives Dia. Care 38 544 550

[26] D. Son 2014 Multifunctional wearable devices for diagnosis and therapy of movement disorders Nature Nanotech 9 397 404

[27] P. CatherwoodS. McCombM. LittleJ McLaughlin(2017 Channel Characterisation for Wearable LoRaWAN Monitors, Loughborough Ant and Prop. Conf. (LAPC) 12-13 Nov. 2017 1 4 IET UK

[28] D. Sanchez-MorilloM.A. Fernandez-GraneroA. Leon-Jimenez 2016 Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma Chronic Resp. Disease 13 264 283

[29] B. PrasadP.K. PrasadY. Sagar 2011 Advances in computer science and information technology Springer Berlin 570 576

[30] A.M. Pescatore 2014 A simple asthma prediction tool for preschool children with wheeze or cough J All. Clin. Immun 133 111 118

[31] A Badnjevic 2015 Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease BMC Med. Inform. Decision Making 15 S1

[32] R. Cellan-Jones 2014 BBC www.bbc.co.uk/news/technology-30290540 19 2 18

[33] V. PalabindalaA. PamarthyN.R. Jonnalagadda 2016 Adoption of electronic health records and barriers J. Community Hosp. Intern. Med. Perspect 6 10.3402/jchimp.v6.32643

[34] D. Wakabayashimarch 2018 NY Times http://www.nytimes.com/2018/03/19/technology/uber-driverless-fatality.html 29 3 18

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