A precision health approach to medication management in neurodevelopmental conditions: a model development and validation study using four international cohorts
Bibliographic record
Abstract
Abstract Psychotropic medications are commonly prescribed to children with neurodevelopmental conditions, but responses vary widely, prescribing is largely off-label, and the expertise required is concentrated in specialized programs. We developed artificial intelligence models to predict prescribing patterns of stimulants, anti-depressants, and anti-psychotics. Feasibility was established in research cohorts by predicting cross-sectional medication use from the Child Behaviour Checklist, with training and internal testing in the Province of Ontario Neurodevelopmental network ( N =598) and external testing in the Healthy Brain Network ( N =1,764) and Adolescent Brain Cognitive Development ( N =2,396) studies. Clinical evaluation used electronic medical records (EMRs) from the Psychopharmacology Program ( N =312) at Holland Bloorview Kids Rehabilitation Hospital to predict the medication class prescribed at a follow-up visit (∼3 months later). In all cohorts, the modelled outcome was the clinician’s prescribing decision, which reflects clinician judgement, family preference, tolerability, and access to care in addition to expected effectiveness, and does not directly measure treatment response or clinical benefit. In the research cohorts, internal testing achieved an area under the receiver operating characteristic curve (median [IQR]) of 0.75 [0.73,0.80] for stimulants, 0.83 [0.78,0.87] for anti-depressants, and 0.79 [0.72,0.86] for anti-psychotics, and external testing confirmed generalizability. In the EMR cohort, values were 0.84 [0.81,0.88] for stimulants, 0.82 [0.77,0.87] for anti-depressants, and 0.87 [0.83,0.91] for anti-psychotics. Findings demonstrate that AI can accurately learn expert prescribing patterns and predict medication prescribing decisions, supporting the potential of data-driven tools to guide personalized medication management for neurodevelopmental conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".