Artificial intelligence-based clinical decision support in pediatrics

Abstract

Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.

Fig. 1. Development of a supervised machine learning algorithm.

Datasets frequently require cleaning and/or preprocessing (such as “one-hot” encoding of categorical variables). Following this, initial analyses are performed to identify distinct variables with the strongest association with the study outcome. A portion of the data may be used as a holdout cohort for internal validation. The remainder is used for model training. Following internal validation, the model may be tested in distinct datasets.

Fig. 2. Functioning of an artificial intelligence clinical decision support (AI-CDS) tool.

Electronic health data exists in a variety of formats, including structured (in discrete fields) or unstructured (such as in narrative notes). The machine learning algorithm may then be applied to these test data. When a desired threshold of disease probability is reached, a best practice alert may be provided to the treatment team.

Fig. 3. Steps involved in the development of artificial intelligence clinical decision support.

Stakeholders should be recruited early in the process to evaluate existing models, identify key priorities and develop a machine learning model. Models should then externally validated with specific consideration to balancing measures, including false positives and negatives and the performance of the model on minorities and/or socioeconomically disadvantaged subgroups. Models may then be implemented into the electronic health record with subsequent evaluation. Models should be studied and compared to standard of care, and if proven favorable, may then be disseminated.