A precision health approach to medication management in neurodivergence: a model development and validation study using four international cohorts
Vandewouw, M. M.; Niroomand, K.; Bokadia, H.; Lenz, S.; Rapley, J.; Arias, A.; Crosbie, J.; Trinari, E.; Kelley, E.; Nicolson, R.; Schachar, R. J.; Arnold, P. D.; Iaboni, A.; Lerch, J. P.; Penner, M.; Baribeau, D.; Anagnostou, E.; Kushki, A.
Show abstract
Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making treatment selection challenging. This study developed artificial intelligence (AI) models to predict successful use of stimulants, anti-depressants, and anti-psychotics. Cross-sectional data from research cohorts (N=4,758) were used to predict medication use from Child Behaviour Checklist scores, validating the feasibility and generalizability of this approach. Longitudinal prediction of medication success was then evaluated using electronic medical records from the Psychopharmacology Program (PPP; N=312) at Holland Bloorview Kids Rehabilitation Hospital. Ensemble models achieved strong performance, indicated by high area under the receiving operating characteristic curve (median [IQR], stimulants: 0.84 [0.81,0.88], anti-depressants: 0.82 [0.77,0.87], anti-psychotics: 0.87 [0.83,0.91]). Findings demonstrate that AI can accurately learn expert prescribing patterns and predict treatment outcomes, supporting the potential of data-driven tools to guide personalized medication management for neurodevelopmental conditions and reduce the trial-and-error burden in clinical practice.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Data-driven characterization of individuals with delayed autism diagnosis 90%
- Impacts of school closures on physical and mental health of children and young people: a systematic review 89%
- Longitudinal associations between weight indices, cognition, and mental health from childhood to early adolescence 89%
Similar papers in this journal
- Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data 93%
- Antipsychotic Polypharmacy and Adverse Drug Reactions Among Adults in a London Mental Health Service, 2008-2018 93%
- Effectiveness of attexis, a Digital Intervention Based on Cognitive Behavioral Therapy for Adults with ADHD: A Randomized Controlled Trial 92%
Similar papers in this journal
- Stimulant medication use and apparent cortical thickness development in attention-deficit/hyperactivity disorder: a prospective longitudinal study 92%
- Adolescents and adults with FOXP1 syndrome show high rates of anxiety and externalizing behaviors but not psychiatric decompensation or skill loss 92%
- Mental and social health of children and adolescents with pre-existing mental or somatic problems during the COVID-19 pandemic lockdown 91%
Similar papers in this journal
- Predictive Patterns of Antidepressant Response from Pre-Treatment Reward Processing using Functional MRI and Deep Learning: Key Results from the EMBARC Randomized Clinical Trial 91%
- Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting 90%
- Altered neurobehavioral reward response predicts psychotic-like experiences in youth exposed to cannabis prenatally 90%
Similar papers in this journal
- Stimulant medication use and development of the dopamine system: a naturalistic long-term follow-up of boys and men with ADHD 90%
- Neurocognition and its association with adverse childhood experiences and familial risk of mental illness 90%
- The selective 5-HT1A receptor biased agonist, NLX-101, corrects anomalous behavioral phenotype in a mouse model of Fragile X syndrome 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.