This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (83) Clinical Informatics (919) Clinical Information and Decision Making (1340) Ontologies, Classifications, and Coding (227)

Published on in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16452, first published .
Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Authors of this article:

Elsie Horne1 Author Orcid Image ;   Holly Tibble1 Author Orcid Image ;   Aziz Sheikh1 Author Orcid Image ;   Athanasios Tsanas1 Author Orcid Image
Article Authors Cited by (25) Tweetations (10) Metrics

Journals

  1. Keenan T. The Hitchhiker’s Guide to Cluster Analysis: Multi Pertransibunt et Augebitur Scientia. Ophthalmology Retina 2020;4(12):1125 View
  2. Genitsaridi E, Hoare D, Kypraios T, Hall D. A Review and a Framework of Variables for Defining and Characterizing Tinnitus Subphenotypes. Brain Sciences 2020;10(12):938 View
  3. Banić I, Lovrić M, Cuder G, Kern R, Rijavec M, Korošec P, Turkalj M. Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children. Asthma Research and Practice 2021;7(1) View
  4. Michel M, Laser K, Dubowy K, Scholl-Bürgi S, Michel E. Metabolomics and random forests in patients with complex congenital heart disease. Frontiers in Cardiovascular Medicine 2022;9 View
  5. Horne E, McLean S, Alsallakh M, Davies G, Price D, Sheikh A, Tsanas A. Defining clinical subtypes of adult asthma using electronic health records: Analysis of a large UK primary care database with external validation. International Journal of Medical Informatics 2023;170:104942 View
  6. Guo D, Su X, Lian Y, Liu L, Wang H. Two‐stage partial image‐text clustering (TPIT‐C). IET Computer Vision 2022;16(8):694 View
  7. Murthy D, Lee J, Dashtian H, Kong G. Influence of User Profile Attributes on e-Cigarette–Related Searches on YouTube: Machine Learning Clustering and Classification. JMIR Infodemiology 2023;3:e42218 View
  8. Kumar R, Khan F, Sharma A, Aziz I, Poddar N. Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic Diseases. Current Medicinal Chemistry 2022;29(1):66 View
  9. Baglione A, Cai L, Bahrini A, Posey I, Boukhechba M, Chow P. Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study. JMIR Medical Informatics 2022;10(6):e30712 View
  10. Patriarca R, Simone F, Di Gravio G. Supporting weather forecasting performance management at aerodromes through anomaly detection and hierarchical clustering. Expert Systems with Applications 2023;213:119210 View
  11. Tsang K, Pinnock H, Wilson A, Shah S. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. Journal of Asthma and Allergy 2022;Volume 15:855 View
  12. Tsanas A, Arora S. Data-Driven Subtyping of Parkinson’s Using Acoustic Analysis of Sustained Vowels and Cluster Analysis: Findings in the Parkinson’s Voice Initiative Study. SN Computer Science 2022;3(3) View
  13. Munns A, Wiffen L, Brown T, Fasulo A, Chauhan M, D'Cruz L, Kaklamanou D, Chauhan A. Capability, Opportunity, and Motivation Model for Behavior Change in People With Asthma: Protocol for a Cross-Sectional Study. JMIR Research Protocols 2023;12:e44710 View
  14. Pikoula M, Kallis C, Madjiheurem S, Quint J, Bafadhel M, Denaxas S, Le N. Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity. PLOS ONE 2023;18(6):e0287264 View
  15. Zahraei H, Schleich F, Gerday S, Guissard F, Paulus V, Henket M, Moermans C, Donneau A, Louis R. A clustering analysis of eosinophilic asthmatics: Two clusters with sharp differences in atopic status and disease severity. Clinical & Experimental Allergy 2023;53(6):672 View
  16. Radhoe T, Agelink van Rentergem J, Torenvliet C, Groenman A, van der Putten W, Geurts H. Finding Similarities in Differences Between Autistic Adults: Two Replicated Subgroups. Journal of Autism and Developmental Disorders 2023 View
  17. Dhafari T, Pate A, Azadbakht N, Bailey R, Rafferty J, Jalali-najafabadi F, Martin G, Hassaine A, Akbari A, Lyons J, Watkins A, Lyons R, Peek N. A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods. Journal of Clinical Epidemiology 2024;165:111214 View
  18. Yang M, Matan-Lithwick S, Wang Y, De Jager P, Bennett D, Felsky D. Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing. Brain Communications 2023;5(2) View
  19. Zahraei H, Schleich F, Louis G, Gerday S, Sabbe M, Bougard N, Guissard F, Paulus V, Henket M, Petre B, Donneau A, Louis R. Evidence for 2 clusters among patients with noneosinophilic asthma. Annals of Allergy, Asthma & Immunology 2024;133(1):57 View
  20. Palomino-Echeverria S, Huergo E, Ortega-Legarreta A, Uson Raposo E, Aguilar F, Peña-Ramirez C, López-Vicario C, Alessandria C, Laleman W, Queiroz Farias A, Moreau R, Fernandez J, Arroyo V, Caraceni P, Lagani V, Sánchez-Garrido C, Clària J, Tegner J, Trebicka J, Kiani N, Planell N, Rautou P, Gomez-Cabrero D. A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis. Journal of Translational Medicine 2024;22(1) View
  21. Ngai S, Cheung C, Ng Y, Lee B, Dupéré V, Wang M, Chen C, Li Y, Zhou Q, Wong L, Zhang X. Pathways from school to work: A sequence analysis of non‐engaged youth. Journal of Adolescence 2024 View
  22. Hassan B, Tayfor N, Hassan A, Ahmed A, Rashid T, Abdalla N. From A-to-Z Review of Clustering Validation Indices. Neurocomputing 2024:128198 View

Books/Policy Documents

  1. Nadif R, Savouré M. Asthma in the 21st Century. View