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Antes Revista del Instituto Nacional de Enfermedades Respiratorias

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2024, Number 3

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Neumol Cir Torax 2024; 83 (3)

Open access protocol and classification of scientific literature on spirometry

Flores-Valadez MA, Michán-Aguirre L, Muñoz-Velasco I, Romero-Pérez M

Language: Spanish
References: 44
Page: 213-223
PDF size: 518.09 Kb.


ABSTRACT

Health personnel who perform lung function tests must be well informed about specialized literature to use the equipment, interpret the results, and establish a diagnosis for monitoring lung diseases. Spirometry is the most widely used lung function test, which is why it is essencial to train technical and health personnel for its correct performance. The objective of this research is to create a protocol to organize and classify the technical and research literature on spirometry, allowing fast and efficient processing for both humans and machines. To achieve our objective, semantic annotations were made in 96 specialized documents on spirometry, with 99 tags categorized into seven key variables analyzed: type of document, access, topic, tests associated with spirometry, stage involved in performing spirometry, functional patterns and diseases studied through. These annotations are available online, are open access, semantic and interoperable, and can be processed by both humans and computers on a user-friendly platform (https://web.hypothes.is/). Due to the characteristics of the annotations, physicians and technicians who perform spirometry can interact with them and other users, thus promoting the analysis of key health information in an open and social manner, which can be useful for practice, research and teaching of pulmonology.


REFERENCES

  1. Kouri A, Dandurand RJ, Usmani OS, Chow CW. Exploring the 175-year history of spirometry and the vital lessons it can teach us today. Eur Respir Rev. 2021;30(162):210081. Available from: https://doi.org/10.1183/16000617.0081-2021

  2. Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, et al. Standardization of spirometry 2019 update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med. 2019;200(8):e70-e88. Available from: https://www.atsjournals.org/doi/10.1164/rccm.201908-1590ST

  3. Stanojevic S, Kaminsky DA, Miller MR, Thompson B, Aliverti A, Barjaktarevic I, et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur Respir J. 2022;60(1):2101499. Available from: https://publications.ersnet.org/content/erj/60/1/2101499

  4. Benítez-Pérez RE, Vázquez-García JC, Sánchez-Gallén E, Salas-Hernández J, Pérez-Padilla R, et al. Impacto de un programa educativo de espirometría en el primer nivel de atención en México. Neumol Cir Tórax. 2021;80(1):29-38. Available from: https://dx.doi.org/10.35366/99451

  5. Culver BH, Graham BL, Coates AL, Wanger J, Berry CE, Clarke PK, et al.; ATS Committee on proficiency standards for pulmonary function laboratories. Recommendations for a standardized pulmonary function report. An Official American Thoracic Society technical statement. Am J Respir Crit Care Med. 2017;196(11):1463-1472. Available from: https://www.atsjournals.org/doi/10.1164/rccm.201710-1981ST

  6. Collen MF. Specialized Medical Databases. Comput Med Databases [Internet]. London: Springer London; 2012. p. 151-182. Available from: http://link.springer.com/10.1007/978-0-85729-962-8_5

  7. Lorenzetti DL, Ghali WA. Reference management software for systematic reviews and meta-analyses: an exploration of usage and usability. BMC Med Res Methodol. 2013;13(1):141. Available from: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-13-141

  8. Michán-Aguirre L, Romero-Pérez MM. Inmediatez en salud: la tecnología RSS. Inv Ed Med. 2024;13(49):120-128. Available from: http://riem.facmed.unam.mx/index.php/riem/article/view/1303

  9. Luque C, Luna JM, Luque M, Ventura S. An advanced review on text mining in medicine. WIREs Data Min Knowl Discov. 2019;9(3):e1302. Available from: http://riem.facmed.unam.mx/index.php/riem/article/view/1303

  10. Kalgotra P, Sharda R. Network analytics: an introduction and illustrative applications in health data science. J Inf Technol Case Appl Res. 2023;25(3):305-315. Available from: https://www.tandfonline.com/doi/full/10.1080/15228053.2023.2187995

  11. Basu K, Sinha R, Ong A, Basu T. Artificial intelligence: how is it changing medical sciences and its future? Indian J Dermatol. 2020;65(5):365-370. Available from: https://doi.org/10.4103/ijd.ijd_421_20

  12. World Wide Web Consortium. W3C. W3C. 2024. Available from: https://www.w3.org/

  13. Cheung KH, Prud'hommeaux E, Wang Y, Stephens S. Semantic Web for Health Care and Life Sciences: a review of the state of the art. Brief Bioinform. 2009;10(2):111-113. Available from: https://academic.oup.com/bib/article-lookup/doi/10.1093/bib/bbp015

  14. Sakor A, Jozashoori S, Niazmand E, Rivas A, Bougiatiotis K, Aisopos F, et al. Knowledge4COVID-19: a semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments' toxicities. J Web Semant. 2023;75:100760. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1570826822000440

  15. Perkel JM. Annotating the scholarly web. Nature. 2015;528(7580):153-154. Available from: https://www.nature.com/articles/528153a

  16. RRID Portal. RRID | SciBot. Available from: https://scicrunch.org/resources/about/scibot

  17. Menke J, Roelandse M, Ozyurt B, Martone M, Bandrowski A. The rigor and transparency index quality metric for assessing biological and medical science methods. iScience. 2020;23(11):101698. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2589004220308907

  18. Mowery DL, Jordan P, Wiebe J, Harkema H, Dowling J, Chapman WW. Semantic annotation of clinical events for generating a problem list. AMIA Annu Symp Proc. 2013;2013:1032-1041.

  19. Wahab N, Miligy IM, Dodd K, Sahota H, Toss M, Lu W, et al. Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations. J Pathol Clin Res. 2022;8(2):116-128. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900128/

  20. ndd. EasyOne Air Operator's Manual V1.1. 2018. Available from: https://henrotech.be/sites/default/files/product/manual/easyone_air%20ENG%20Manual.pdf

  21. ndd. EasyOne ProTM LAB Manual del Operador. 2012. Available from: https://nddmed.com/_Resources/Persistent/6f014bcf8df7622039fb234f96fe70fe6d97667c/easyone-pro-manual-v04b.pdf

  22. García-Río F, Calle M, Burgos F, Casan P, del Campo F, Galdiz JB, et al. Spirometry. Arch Bronconeumol. 2013;49(9):388-401. Available from: http://archbronconeumol.org/en-spirometry-articulo-S1579212913001341

  23. Devos FC, Maaske A, Robichaud A, Pollaris L, Seys S, Lopez CA, et al. Forced expiration measurements in mouse models of obstructive and restrictive lung diseases. Respir Res. 2017;18(1):123. Available from: http://respiratory-research.biomedcentral.com/articles/10.1186/s12931-017-0610-1

  24. Prisk GK, Fine JM, Cooper TK, West JB. Vital capacity, respiratory muscle strength, and pulmonary gas exchange during long-duration exposure to microgravity. J Appl Physiol. 2006;101(2):439-447. Available from: https://www.physiology.org/doi/10.1152/japplphysiol.01419.2005

  25. Guiard Y. Understanding the within-individual variability of forced vital capacity: an exploitation of the nhanes iii spirometry data. 2021. Available from: https://hal.science/hal-03316189

  26. Feher J. Lung volumes and airway resistance. Quant Hum Physiol. Elsevier; 2012. p. 563-571. Available from: https://linkinghub.elsevier.com/retrieve/pii/B978012382163800061X

  27. Nichols DP. Functional assessment of asthma. Pediatr Allergy Princ Pract. Elsevier; 2016. p. 267-275.e2. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780323298759000306

  28. Narayanan M, Silverman M. Long-term consequences of childhood respiratory disease. Kendig Chernick's Disord Respir Tract Child. Elsevier; 2012. p. 278-283. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9781437719840000176

  29. Hypothesis. Hypothesis. 2023. Available from: https://web.hypothes.is/

  30. Peroni S, Shotton D. FaBiO and CiTO: ontologies for describing bibliographic resources and citations. J Web Semant. 2012;17:33-43. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1570826812000790

  31. National Library of Medicine. Medical Subject Headings. 2024. Available from: https://www.nlm.nih.gov/mesh/meshhome.html

  32. Garnier S, Ross N, Rudis B, Filipovic-Pierucci A, Galili T, timelyportfolio, et al. sjmgarnier/viridis: CRAN release v0.6.3. Zenodo; 2023. Available from: https://zenodo.org/record/7890878

  33. Siegel MG, Rossi MJ, Lubowitz JH. Artificial intelligence and machine learning may resolve health care information overload. Arthrosc J Arthrosc Relat Surg. 2024;40(6):1721-1723. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0749806324000124

  34. Choi S. The coronavirus disease 2019 infodemic: a concept analysis. Front Public Health. 2024;12:1362009. Available from: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1362009/full

  35. Ishizumi A, Kolis J, Abad N, Prybylski D, Brookmeyer KA, Voegeli C, et al. Beyond misinformation: developing a public health prevention framework for managing information ecosystems. Lancet Public Health. 2024;9(6):e397-e406. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2468266724000318

  36. Bandrowski A, Brush M, Grethe JS, Haendel MA, Kennedy DN, Hill S, et al. The resource identification initiative: A cultural shift in publishing. Neuroinform. 2016;14(2):169-182. Available from: http://link.springer.com/10.1007/s12021-015-9284-3

  37. Judell. SciBot: Machine and human annotators working together. Hypothesis. 2016. Available from: https://web.hypothes.is/blog/introducing-developer-api-tokens/

  38. Goller CC, Vandegrift M, Cross W, Smyth DS. Sharing notes is encouraged: annotating and cocreating with Hypothes.is and Google Docs. J Microbiol Biol Educ. 2021;22(1):ev22i1.2135. Available from: https://journals.asm.org/doi/10.1128/jmbe.v22i1.2135

  39. Salehipour D, Farncombe KM, Andric V, Ansar S, Delong S, Li E, et al. Developing a disease-specific annotation protocol for VHL gene curation using Hypothes.is. Database. 2023;2023:baac109. Available from: https://doi.org/10.1093/database/baac109.

  40. Zeng ML. Knowledge Organization Systems (KOS). Knowl Organ. 2008;35(2-3):160-182. Available from: https://www.nomos-elibrary.de/index.php?doi=10.5771/0943-7444-2008-2-3-160

  41. Mazzocchi F. Knowledge Organization System (KOS): an introductory critical account. Knowl Organ. 2018;45(1):54-78. Available from: https://www.nomos-elibrary.de/index.php?doi=10.5771/0943-7444-2018-1-54

  42. Hodge GM. Systems of knowledge organization for digital libraries: beyond traditional authority files. Digital Library Federation; 2000.

  43. Reichmann S, Wieser B. Open science at the science–policy interface: bringing in the evidence? Health Res Policy Syst. 2022;20(1):70. Available from: https://health-policy-systems.biomedcentral.com/articles/10.1186/s12961-022-00867-6

  44. Hypothesis. What is the license on annotations? Hypothesis. 2023. Available from: https://web.hypothes.is/help/what-is-the-license-on-annotations




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