Artificial intelligence in pediatric allergy research

Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S et al (2021) Artificial intelligence: a powerful paradigm for scientific research. Innovation (Camb) 2:100179

PubMed  Google Scholar 

Kulikowski CA (2019) Beginnings of Artificial Intelligence in Medicine (AIM): computational artifice assisting scientific inquiry and clinical art - with reflections on present AIM challenges. Yearb Med Inform 28:249–256

Article  PubMed  PubMed Central  Google Scholar 

Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN et al (2023) Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23:689

Article  PubMed  PubMed Central  Google Scholar 

Reddy S (2024) Generative AI in healthcare: an implementation science informed translational path on application, integration and governance. Implement Sci 19:27

Article  PubMed  PubMed Central  Google Scholar 

Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A et al (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493–500

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen A, Liu L, Zhu T (2024) Advancing the democratization of generative artificial intelligence in healthcare: a narrative review. J Hospital Manag Health Policy 8

Macdonald C, Adeloye D, Sheikh A, Rudan I (2023) Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis. J Glob Health 13:01003

Article  PubMed  PubMed Central  Google Scholar 

Custovic A, Custovic D, Fontanella S (2024) Understanding the heterogeneity of childhood allergic sensitization and its relationship with asthma. Curr Opin Allergy Clin Immunol 24:79–87

Article  PubMed  PubMed Central  Google Scholar 

Ahuja AS (2019) The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 7:e7702

Article  PubMed  PubMed Central  Google Scholar 

van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC et al (2023) Current state and prospects of artificial intelligence in allergy. Allergy 78:2623–2643

Article  PubMed  Google Scholar 

Serebrisky D, Wiznia A (2019) Pediatric asthma: a global epidemic. Ann Glob Health 85(1):6

Article  PubMed  PubMed Central  Google Scholar 

Zhang D, Zheng J (2022) The burden of childhood asthma by age group, 1990–2019: a systematic analysis of global burden of disease 2019 data. Front Pediatr 10:823399

Article  PubMed  PubMed Central  Google Scholar 

Licari A, Magri P, De Silvestri A, Giannetti A, Indolfi C, Mori F et al (2023) Epidemiology of allergic rhinitis in children: a systematic review and meta-analysis. J Allergy Clin Immunol Pract 11:2547–2556

Article  PubMed  Google Scholar 

Mallol J, Crane J, von Mutius E, Odhiambo J, Keil U, Stewart A (2013) The International Study of Asthma and Allergies in Childhood (ISAAC) phase three: a global synthesis. Allergol Immunopathol 41:73–85

Article  CAS  Google Scholar 

Hoque F, Poowanawittayakom N (2023) Future of AI in medicine: new opportunities & challenges. Mo Med 120:349

PubMed  PubMed Central  Google Scholar 

Eigenmann P, Akenroye A, Atanaskovic Markovic M, Candotti F, Ebisawa M, Genuneit J et al (2023) Pediatric Allergy and Immunology (PAI) is for polishing with artificial intelligence, but careful use. Pediatr Allergy Immunol 34:e14023

Article  PubMed  Google Scholar 

Ferrante G, Licari A, Fasola S, Marseglia GL, La Grutta S (2021) Artificial intelligence in the diagnosis of pediatric allergic diseases. Pediatr Allergy Immunol 32:405–413

Article  PubMed  Google Scholar 

Razavian N, Knoll F, Geras KJ (2020) Artificial intelligence explained for nonexperts. Semin Musculoskelet Radiol 24:3–11

Article  PubMed  PubMed Central  Google Scholar 

Fazakis N, Kanas VG, Aridas CK, Karlos S, Kotsiantis S (2019) Combination of active learning and semi-supervised learning under a self-training scheme. Entropy (Basel) 21(10):988. https://doi.org/10.3390/e21100988

Khezeli K, Siegel S, Shickel B, Ozrazgat-Baslanti T, Bihorac A, Rashidi P (2023) Reinforcement learning for clinical applications. Clin J Am Soc Nephrol 18:521–523

Article  PubMed  PubMed Central  Google Scholar 

Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. A Bradford Book, Cambridge, MA, USA, chapter 1, pp 1–13

Kufel J, Bargieł-Łączek K, Kocot S, Koźlik M, Bartnikowska W, Janik M et al (2023) What is machine learning, artificial neural networks and deep learning?-Examples of practical applications in medicine. Diagnostics (Basel). 13(15):2582

Article  PubMed  PubMed Central  Google Scholar 

Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629

Article  PubMed  PubMed Central  Google Scholar 

Montesinos López OA, Montesinos López A, Crossa J (2022) Fundamentals of artificial neural networks and deep learning. multivariate statistical machine learning methods for genomic prediction: Springer, p 379–425

Murtagh F (2015) A brief history of cluster analysis. CRC Press, Handbook of Cluster Analysis, pp 21–30

Google Scholar 

Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (SODA '07). Society for Industrial and Applied Mathematics, USA, 1027–1035. https://doi.org/10.1145/1283383.1283494

Suganya R, Shanthi R (2012) Fuzzy c-means algorithm-a review. Int J Sci Res Publ 2:1

Google Scholar 

Bezdek JC (2013) Objective function clustering. In: Pattern recognition with fuzzy objective function algorithms. Advanced Applications in Pattern Recognition. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-0450-1_3

Malizia V, Ferrante G, Cilluffo G, Gagliardo R, Landi M, Montalbano L et al (2021) Endotyping seasonal allergic rhinitis in children: a cluster analysis. Front Med (Lausanne) 8:806911

Article  PubMed  Google Scholar 

Lauffer F, Baghin V, Standl M, Stark SP, Jargosch M, Wehrle J et al (2021) Predicting persistence of atopic dermatitis in children using clinical attributes and serum proteins. Allergy 76:1158–1172

Article  CAS  PubMed  Google Scholar 

Xu J, Bian J, Fishe JN (2023) Pediatric and adult asthma clinical phenotypes: a real world, big data study based on acute exacerbations. J Asthma 60:1000–1008

Article  PubMed  Google Scholar 

Bakker DS, de Graaf M, Nierkens S, Delemarre EM, Knol E, van Wijk F et al (2022) Unraveling heterogeneity in pediatric atopic dermatitis: identification of serum biomarker based patient clusters. J Allergy Clin Immunol 149:125–134

Article  CAS  PubMed  Google Scholar 

Yeh YL, Su MW, Chiang BL, Yang YH, Tsai CH, Lee YL (2018) Genetic profiles of transcriptomic clusters of childhood asthma determine specific severe subtype. Clin Exp Allergy 48:1164–1172

Article  CAS  PubMed  Google Scholar 

Schubert E, Rousseeuw PJ (2021) Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms. Inf Syst 101:101804

Article  Google Scholar 

Schubert E, Rousseeuw PJ (2019) Faster k-medoids clustering: improving the PAM, CLARA, and CLARANS algorithms. In: Similarity search and applications: 12th International Conference, SISAP 2019, Newark, NJ, USA, Proceedings 12. Springer International Publishing, pp 171–187. https://arxiv.org/abs/1810.05691

Ng RT, Han J (2002) CLARANS: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14:1003–1016

Article  Google Scholar 

Kaufman L, Rousseeuw PJ (2009) [Chapter 2] Partitioning around medoids (Program PAM). In: Kaufman L, Rousseeuw PJ (eds) Finding groups in data, pp 68–125. https://doi.org/10.1002/9780470316801.ch2, [Chapter 3] Clustering large applications (Program CLARA). In: Kaufman L, Rousseeuw PJ (eds) Finding groups in data, pp 126–163. https://doi.org/10.1002/9780470316801.ch3

Krishnapuram R, Joshi A, Nasraoui O, Yi L (2001) Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Trans Fuzzy Syst 9:595–607

Article  Google Scholar 

Preud’homme G, Duarte K, Dalleau K, Lacomblez C, Bresso E, Smaïl-Tabbone M et al (2021) Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark. Sci Rep 11:4202

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kaufman L, Rousseeuw PJ (1986) Clustering large data sets. In: Gelsema ES, Kanal LN (eds) Pattern Recognition in Practice. Elsevier, Amsterdam, pp 425–437

Chapter  Google Scholar 

Pina AF, Meneses MJ, Sousa-Lima I, Henriques R, Raposo JF, Macedo MP (2023) Big data and machine learning to tackle diabetes management. Eur J Clin Invest 53:e13890

Article  PubMed 

Comments (0)

No login
gif