Researchers trained machine learning (ML) models to analyze RNA molecular signatures in patients’ blood.
By Hugo Francisco de SouzaAug 28 2023Reviewed by Lily Ramsey, LLM In a recent study published in the Med Journal, researchers trained machine learning models to analyze RNA molecular signatures in patients’ blood and evaluated their performance in distinguishing between common infectious pediatric diseases.
The limitations of today’s pediatric diagnoses Children seeking medical care most commonly suffer from inflammatory and infectious diseases in hospital and community settings. Most severe infections are localized in hard-to-access sites , resulting in false negative reports despite severe clinical infection symptoms. Inflammatory conditions, including Kawasaki disease and juvenile idiopathic arthritis, do not currently have tests to confirm or refute diagnosis, resulting in severe delays in treatment initiation, or worse, disease misidentification.
A growing body of research elucidates that transcriptional signatures in whole-blood samples can rapidly and accurately distinguish between bacterial and viral infections, dengue, malaria, rotavirus, respiratory syncytial virus, tuberculosis , and inflammatory conditions, including systemic lupus erythematosus and KD.
To discover the biomarker panel used for model training, 12 publicly available microarray datasets of children with acute febrile illness and healthy controls were used. Cost-sensitivity is a model penalization algorithm that uses the consensus judgment of multiple field experts to assign ‘weightage’ to the demerits of disease misidentification or treatment initiation delays. This allowed for the prioritization of predictions in favor of conditions for which misdiagnosis consequences are highest.
The final ML model was cross-validated on an independent dataset comprising whole-blood RNA-seq data from 411 patients covering all broad diagnostic classes and 18 under-study diseases to validate the LASSO-Ridge hybrid model performance. Test set prediction results revealed that ML models can reliably predict most diagnostic classes, albeit with prediction performance being a function of training sample size.
“To ensure clinical utility, further development of the approach will require large prospective patient cohorts, with consistent, detailed, and accurate clinical phenotypes. By expanding the range of conditions included in the discovery of the transcript panels, it may be possible to improve the treatment of a large number of patients, particularly for rare and under-diagnosed conditions for which early detection and thus treatment could have a significant benefit.
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