Development of a computerized adaptive testing for ADHD using Bayesian networks: An attempt at classification.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to classify ADHD symptom. Probabilities were given within the referenced paper. The vertices are:
ADHD symptom severity (No, Few, Moderate, Risk);
Carelessness (Never, Sometimes, Often, Very Often);
Difficulty sustaining attention in activities (Never, Sometimes, Often, Very Often);
Doesn't listen (Never, Sometimes, Often, Very Often);
No follow through (Never, Sometimes, Often, Very Often);
Can't organize (Never, Sometimes, Often, Very Often);
Avoids/dislikes tasks requiring sustained mental effort (Never, Sometimes, Often, Very Often);
Loses important items (Never, Sometimes, Often, Very Often);
Easily distractible (Never, Sometimes, Often, Very Often);
Forgetful in daily activities (Never, Sometimes, Often, Very Often);
Squirms and fidgets (Never, Sometimes, Often, Very Often);
Can't stay seated (Never, Sometimes, Often, Very Often);
Runs/climbs excessively (Never, Sometimes, Often, Very Often);
Can't play/work quietly (Never, Sometimes, Often, Very Often);
On the go, "driven by a motor" (Never, Sometimes, Often, Very Often);
Talks excessively (Never, Sometimes, Often, Very Often);
Blurts out answers (Never, Sometimes, Often, Very Often);
Can't wait for turn (Never, Sometimes, Often, Very Often);
Intrudes/interrupts others (Never, Sometimes, Often, Very Often);
Jiang, Z., Ma, W., Flory, K., Zhang, D., Zhou, W., Shi, D., ... & Liu, R. (2023). Development of a computerized adaptive testing for ADHD using Bayesian networks: An attempt at classification. Current Psychology, 42(22), 19230-19240.