Evaluating dimensionality reduction of comorbidities for predictive modeling in individuals with neurofibromatosis type 1.

Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).

Materials And Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes.

Precocious Puberty in Children with Neurofibromatosis Type 1.

This multi-institutional, descriptive study of 19 children with neurofibromatosis 1 examines the link between optic pathway gliomas (OPGs) and central precocious puberty (CPP). We report that CPP can arise without OPG chiasmal involvement and that prior OPG chemotherapy does not prevent the development of CPP.

Early Young Adult Death Underscores the Need for Adolescent and Young Adult Transition Programs in Neurofibromatosis Type 1.

There are few centers with combined pediatric and adult neurofibromatosis 1 practices and transition of care programming. Using an electronic health records-based approach, we found an early death peak in the fourth decade of life largely owing to malignancy, underscoring the need for integrated neurological training and practice across the lifespan.

Integrating priorities at the intersection of cancer and neuroscience.

Cancer neuroscience is a rapidly growing multidisciplinary field that conceptualizes tumors as tissues fully integrated into the nervous system. Recognizing the complexity and challenges in this field is of fundamental importance to achieving the goal of translational impact for cancer patients. Our commentary highlights key scientific priorities, optimal training settings, and roadblocks to translating scientific findings to the clinic in this emerging field, aiming to formulate a transformative and cohesive path forward.