The future of personalized medicine and its application to children and adults with neurofibromatosis type 1 (NF1) relies on the establishment of risk factors to better predict what clinical features are most likely to develop in any given individual. Working with scientists in the Washington University Institute for Informatics (link: https://informatics.wustl.edu/), researchers in the Neurofibromatosis Center applied machine learning to identify predictive factors for brain tumors and attention deficits in children with NF1.
Dr. Philip Payne, Director of the Institute for Informatics, and his colleagues, Drs. Aditi Gupta and Randi Foraker, joined forces with Drs. Stephanie Morris and David Gutmann, to use electronic health records
data and advanced informatic methods. Combining their expertise in clinical medicine and bioinformatics, they were able to develop classification models to predict a diagnosis of optic pathway glioma or attention deficit.
While still in the early stages, this study opens the door to larger explorations across institutions and the potential to create risk assessment algorithms for future use to predict what medical problems might arise in children and adults with NF1.
This study was published in Neurology: Clinical Practice. https://pubmed.ncbi.nlm.nih.gov/34987881/