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

Abstract

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. IMPACT: Critical care units generate large amounts of under-utilized data that can be processed through artificial intelligence. This review summarizes the machine learning and artificial intelligence techniques currently being used to process clinical data. The review highlights the applications and limitations of these techniques within a clinical context to aid providers in making more informed decisions at the bedside.

Fig. 1. Types of machine learning.

Examples of only a few types of machine learning, including subcategories of supervised and unsupervised machine learning.

Fig. 2. K-fold cross-validation.

Example of K-fold cross validation. A different subset of the data is used for training and validation in each fold, and performance is based off combined performance in the validation folds.

Fig. 3. Confusion Matrix.

Example of a 2x2 confusion matrix, comparing prediction to ground truth.