Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach
Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high
degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood …
degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood …
Genomic machine learning meta-regression: insights on associations of study features with reported model performance
EJ Barnett, DG Onete, A Salekin… - IEEE/ACM Transactions …, 2023 - ieeexplore.ieee.org
Many studies have been conducted with the goal of correctly predicting diagnostic status of
a disorder using the combination of genomic data and machine learning. It is often hard to …
a disorder using the combination of genomic data and machine learning. It is often hard to …
Advancing Clinical Psychiatry: Integration of Clinical and Omics Data Using Machine Learning
B Qi, YJ Trakadis - Biological Psychiatry, 2023 - biologicalpsychiatryjournal.com
In the current issue of Biological Psychiatry, Habets et al.(1) focus on predicting the 2-year
remission status for major depressive disorder (MDD) at an individual subject level, using …
remission status for major depressive disorder (MDD) at an individual subject level, using …
Context Matters: Using Genomic Knowledge to Improve Disorder Classification Models
EJ Barnett - 2023 - soar.suny.edu
Despite heritability estimates that suggest a high ceiling for the classification of many
complex genetic disorders, current models have only been moderately successful at …
complex genetic disorders, current models have only been moderately successful at …