Análise do deep learning em cuidados de saúde

Flávio Vaz Machado, Liszety Guimarães Emmerick, Roberto Carlos Lyra da Silva, Luiza Cerqueira Reis da Costa, Fernanda Rodrigues da Silva, Alexandre Fernando Coutinho da Silva, Elizangela Aparecida da Silva de Laffitte Alves Alves, Ilda Cecília Moreira da Silva

Resumo


Objetivo: analisar a aplicabilidade e os benefícios do Deep Learning na área de cuidados de saúde. Método: trata-se de um estudo descritivo, tipo análise reflexiva, com consulta a artigos entre os anos de 2014 a 2019, publicados em inglês e revisado por pares, no Portal de Periódicos da CAPES, com a equação de busca (“Deep Learning” AND (“Health Care” OR Health-care OR Healthcare)). Apresentaram-se os resultados em forma de figura seguida da análise descritiva. Resultados: revela-se que 15 artigos descrevem a aplicabilidade do Deep Learning na área de cuidados de saúde. Analisou-se, por este artigo, o emprego do Deep Learning em diferentes áreas referentes aos cuidados de saúde, destacando os benefícios encontrados pelos autores dos selecionados por meio da revisão de literatura. Conclusão: sugere-se o emprego do Deep Learning na área de cuidados de saúde diante dos benefícios identificados nos artigos selecionados como: a previsão dos estágios das doenças; a identificação precisa de mutações patológicas e o suporte aos médicos e aos enfermeiros em suas atividades diárias. Descritores: Benefícios; Deep Learning; Cuidados de Saúde; Doenças; Médicos; Enfermeiros.

Abstract

Objective: to analyze the applicability and benefits of Deep Learning in health care. Method: this is a descriptive study, reflective analysis, with articles from 2014 to 2019, published in English and peer-reviewed, in the CAPES Journal Portal, with the search equation (“Deep Learning ”AND (“ Health Care ”OR Health-care OR Healthcare)). The results were presented in figure form followed by descriptive analysis. Results: it is revealed that 15 articles describe the applicability of Deep Learning in the health care area. This article analyzed the use of Deep Learning in different areas related to health care, highlighting the benefits found by the authors of those selected through the literature review. Conclusion: it is suggested the use of Deep Learning in health care in view of the benefits identified in the articles selected as: the prediction of disease stages; precise identification of pathological mutations and support to doctors and nurses in their daily activities. Descriptors: Benefits; Deep Learning; Health Care; Diseases; Physicians; Nurses.

Resumen

Objetivo: analizar la aplicabilidad y los beneficios del Deep Learning en la atención médica. Método: se trata de un estudio descriptivo, tipo análisis reflexivo, con artículos de 2014 a 2019, publicados en inglés y revisados por pares, en el Portal de la revista CAPES, con la ecuación de búsqueda (“Deep Learning” Y (“Health Care” O Health-care O Healthcare)). Los resultados se presentaron en forma de figura seguida de un análisis descriptivo. Resultados: se revela que 15 artículos describen la aplicabilidad del Deep Learning en el área de la atención médica. Este artículo analizó el uso del Deep Learning en diferentes áreas relacionadas con la atención de la salud, destacando los beneficios encontrados por los autores de los seleccionados a través de la revisión de la literatura. Conclusión: se sugiere el uso de Deep Learning en la atención de la salud en vista de los beneficios identificados en los artículos seleccionados como: la predicción de las etapas de la enfermedad; identificación precisa de mutaciones patológicas y apoyo a médicos y enfermeros en sus actividades diarias. Descriptores: Beneficios; Deep Learning; Cuidados de la Salud; Enfermidades; Médicos; Enfermeros.


Palavras-chave


Benefícios; Deep Learning; Cuidados de Saúde; Doenças; Médicos; Enfermeiros.

Texto completo:

PDF (English) PDF

Referências


Purushotham S, Meng C, Che Z, Liu Y. Benchmarking deep learning models on large healthcare datasets. J Biomed Inform. 2018 July; 83:112–34. Doi: https://doi.org/10.1016/j.jbi.2018.04.007

Bini SA. Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? J Arthroplasty. 2018 Aug;33(8):2358–61. Doi: https://doi.org/10.1016/j.arth.2018.02.067

World Health Organization. Resolutions and Decisions WHA58.28 eHealth [Internet]. Geneva: WHO; 2005 [cited 2018 Dec 15]. Available from: https://apps.who.int/iris/bitstream/handle/10665/20378/WHA58_28-en.pdfjsessionid=29FCA109F3711C9A1CDAFC2DC48254EE?sequence=1

Organização Mundial da Saúde. Chamada da OMS para tecnologias inovadoras em saúde para situação de recursos limitados [Internet]. Brasília: PAHO; 2013 [cited 2018 Aug 10]. Available from: https://www.paho.org/bra/index.php?option=com_content&view=article&id=3175:chamada-da-oms-para-tecnologias-inovadoras-em-saude-para-situacao-de-recursos-limitados&Itemid=838

Goodfellow Y, Bengio A. Courville, Deep Learning. Cambridge MIT Press; 2016.

Squire L, Berg D, Bloom FE, Lac SD, Ghosh A, Spitzer NC. Fundamental Neuroscience. California: Academic Press; 2012.

Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018 Nov; 19(6):1236–46. Doi: 10.1093/bib/bbx044

Landset S, Khoshgoftaar TM, Richter AN, Hasanin T. A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J Big Data. 2015 Nov; 2:24. Doi: http://dx.doi.org/10.1186/s40537-015-0032-1

Liu C, Liu X, Wu F, Xie M, Feng Y, Hu C. Can Watson for Oncology replace oncologists: A comparative study between Watson for Oncology and our multidisciplinary tumor board (Preprint). J Med Internet Res. 2018 Sept; 20(9):e11087. Doi: http://dx.doi.org/10.2196/11087

Poole DL, Mackworth A, Goebel RG. Computational Intelligence and Knowledge [Internet]. New York: Oxford University Press; 1998 [cited 2018 Aug 10]. Available from: https://www.cs.ubc.ca/~poole/ci/ch1.pdf

Bughin J, Hazan E, Ramaswamy S, Chui M, Allas T, Dahlstrom P, et al. Artificial Intelligence: the next digital frontier? Chicago: McKinsey € Global Institute; 2017 [cited 2019 June 15];

Available from: https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx

Widmayer CE. John Sloan Dickey: a Chronicle of His Presidency of Dartmouth College. Hanover: Dartmouth College; 1991.

Miranda ACC. Análise do uso do Portal de Periódicos da CAPES na perspectiva de mestres e doutores formados pelo Programa de Pós-graduação em Administração da UFRN [Internet] [dissertation]. Natal: Universidade Federal do Rio Grande do Norte; 2014 [cited 2018 Aug 10]. Available from: https://periodicos.ufrn.br/bibliocanto/article/view/11567

Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009;6(7):e1000097. Doi: 10.1371/journal.pmed1000097

Tong C, Medsker L, Cheng L, Jafari A, Wang X. Introduction to the special issue on deep learning for biomedical and healthcare applications. Neural Comput Appl.2018 Sept; 30(7):2015–6. Doi: http://dx.doi.org/10.1007/s00521-018-3694-8

Pham T, Tran T, Phung D, Venkatesh S. Predicting healthcare trajectories from medical records: A deep learning approach. J Biomed Inform. 2017 May; 69:218–29. Doi: 10.1016/j.jbi.2017.04.001

Song Q, Zhao L, Luo X, Dou X. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. J Healthc Eng. 2017 Mar/Aug; 1–7. Doi: http://dx.doi.org/10.1155/2017/8314740

Dai Y, Wang G. A deep inference learning framework for healthcare. Pattern Recognition Letters. 2018 Feb. Doi: http://dx.doi.org/10.1016/j.patrec.2018.02.009

Yuan W, Li C, Guan D, Han G, Khattak AM. Socialized healthcare service recommendation using deep learning. Neural Comput Appl. 2018 Mar; 30(7):2071–82. Doi: http://dx.doi.org/10.1007/s00521-018-3394-4

Liu B, Dai X, Gong H, Guo Z, Liu N, Wang X, et al. Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model. J Sports Med. 2018 July/Nov; 1–9. Doi: http://dx.doi.org/10.1155/2018/5214067

Lee JH, Kim KG. Applying Deep Learning in Medical Images: The Case of Bone Age Estimation. Healthc Inform Res. 2018 Jan; 24(1):86. Doi: 10.4258/hir.2018.24.1.86

Alhussein M, Muhammad G. Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework. IEEE Access .2018 July; 6:41034–41. Doi: http://dx.doi.org/10.1109/access.2018.2856238

Gao Y, Xiang X, Xiong N, Huang B, Lee HJ, Alrifai R, et al. Human Action Monitoring for Healthcare Based on Deep Learning. IEEE Access. 2018 Nov; 6:1. Doi: http://dx.doi.org/10.1109/access.2018.2869790

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, Depristo M, Chou K, et al. A guide to deep learning in healthcare. Nature Med. 2019 Jan; 25(1):24(6). Doi: http://dx.doi.org/10.1038/s41591-018-0316-z

Tran GS, Nghiem TP, Nguyen VT, Luong CM, Burie J-C. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss. J Healthc Eng. 2019 Feb 4; 2019:1–9. Doi: http://dx.doi.org/10.1155/2019/5156416

Pandey SK, Janghel RR. Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: a review. Neural Processing Letters. 2019 Jan; 01-29. Doi: http://dx.doi.org/10.1007/s11063-018-09976-2

Kong Z, Li T, Luo J, Xu S. Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning. J Healthc Eng. 2019 Jan; 1–10. Doi: https://doi.org/10.1155/2019/2912458

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA [Internet]. American Medical Association (AMA); 2016 Dec 13;316(22):2402. Available from: http://dx.doi.org/10.1001/jama.2016.17216

Lipton ZC, Kale DC, Elkan C., et al. Learning to diagnose with LSTM recurrent neural networks. In: International Conference on Learning Representations, San Diego, CA, USA, 2015, 1–18. Doi:http://arxiv.org/abs/1511.03677




DOI: https://doi.org/10.5205/1981-8963.2019.242121



Licença Creative Commons
Esta obra está licenciada sob uma licença Creative Commons Atribuição 4.0 Internacional.

 

INDEXADORES E BASES BIBLIOGRÁFICAS:

 doajPeriódicoscapes

bvs elsevier nlm diadorim periodicaabec

 

cinahl citefactor cuidenplusb socolar ulrichs sumarios