The use of machine learning and artificial intelligence within pediatric critical care
Neel Shah1, Ahmed Arshad2, Monty B Mazer3
1Department of Pediatrics, Washington University, St. Louis, MO, USA. Neel.Shah@Wustl.edu.
Insights
Machine learning and artificial intelligence can help pediatric critical care by analyzing complex patient data to define disease subtypes. This approach aids clinicians in making better-informed decisions at the bedside.
Area of Science:
- Pediatric Critical Care
- Medical Informatics
- Computational Biology
Background:
- Pediatric critical care faces challenges in precision medicine due to disease heterogeneity across age groups.
- This heterogeneity complicates the design and execution of pediatric randomized controlled trials.
- Current methods struggle to accurately define and subclassify pediatric disease phenotypes.
Purpose of the Study:
- To review machine learning (ML) and artificial intelligence (AI) techniques for clinical data modeling in pediatric critical care.
- To highlight the differences and roles of various ML/AI techniques in the clinical setting.
- To describe clinical decision support systems that leverage ML/AI.
Main Methods:
- Review of current ML and AI techniques applied to clinical data.
- Analysis of applications and limitations of these techniques in pediatric critical care.
- Examination of clinical decision support systems utilizing ML/AI.
Main Results:
- ML and AI offer promising approaches to interpret complex clinical data in pediatric critical care.
- These techniques can assist in overcoming challenges related to disease phenotype heterogeneity.
- Various ML/AI methods have demonstrated utility in clinical data modeling and decision support.
Conclusions:
- AI and ML can process the vast amounts of data generated in critical care units.
- Understanding the applications and limitations of ML/AI is crucial for effective bedside decision-making.
- These technologies have the potential to empower clinicians with more informed choices.
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.
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