973: Latent Class Analysis of Pediatric Patients With Near-Fatal Asthma

Kolli, Sneha; Rodenbough, Anna; Jones, Kaitlin; Mason, Carrie; Opolka, Cydney; Grunwell, Jocelyn R.; Fitzpatrick, Anne M.

doi:10.1097/01.ccm.0000729780.23521.f5

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1). [11][12][13][14][15][31][32][33][34][35] Unsupervised machine learning may serve as a data exploration tool as it requires less manual intervention as it involves unlabeled data. While these techniques can yield previously undiscovered patterns, the groupings may not necessarily be clinically meaningful without clinician insight.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…Latent class or profile analysis is the most used unsupervised machine learning technique in pediatric research. [32][33][34][35] Latent class or profile analysis allows the detection of a possible unmeasured group within a population by inferring patterns or indicators from the observed variables. 36 This differs from cluster analysis which uses a distance from a specific measure to assign grouping, while latent class or profile analysis estimates the probability of each unit belonging to a class.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

The use of machine learning and artificial intelligence within pediatric critical care

et al. 2022

View full text Add to dashboard Cite

The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the ability to perform randomized controlled trials in pediatrics. One approach to overcome these inherent challenges include the use of machine learning algorithms that can assist in generating more meaningful interpretations from clinical data. This review summarizes machine learning and artificial intelligence techniques that are currently in use for clinical data modeling with relevance to pediatric critical care. Focus has been placed on the differences between techniques and the role of each in the clinical arena. The various forms of clinical decision support that utilize machine learning are also described. We review the applications and limitations of machine learning techniques to empower clinicians to make informed decisions at the bedside.

show abstract