Previously submitted to: JMIR AI (no longer under consideration since Feb 24, 2025)
Date Submitted: Mar 16, 2023
Open Peer Review Period: Mar 16, 2023 - May 11, 2023
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Clinical Diagnosis of Attention Deficit Hyperactivity disorder (ADHD) using Artificial Intelligence
ABSTRACT
Background:
Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by a persistent pattern of inattention, hyperactivity, and/or impulsivity that causes significant impairment across various domains. Delayed diagnosis and treatment for ADHD can be harmful to people, leading to broader mental health and social care difficulties. A diagnostic decision tool was developed using Artificial Intelligence (AI) to assist clinicians with the diagnosis of ADHD in adults. This tool is built by incorporating both expert clinician knowledge using a knowledge-based approach and historical clinical data using machine learning leading to an efficient hybrid approach.
Objective:
To evaluate the efficacy of the AI tool in clinical practice in the diagnosis of adult ADHD.
Methods:
Routine clinical data were collected by a specialist adult ADHD Clinic from people undergoing an assessment. A hybrid diagnostic tool for adult ADHD is proposed that combines a knowledge representation (KR) and machine learning (ML) model. The former encodes expert medical knowledge about ADHD diagnosis into a rule-based expert system; the latter is the optimal model after training, fine-tuning, and testing an ensemble of classification models on the acquired clinical data using 285 patient records. Each model predicts the diagnostic outcome by suggesting a positive or negative ADHD case, whereas in the case of rule-based and hybrid models it is also possible to yield a “expert” result.
Results:
Accuracy was expressed using the ratio of true/false positive/negatives (f1 score) and is calculated as 90.18%, 83.67% and 94.39% for the KR, ML and hybrid model, respectively. The proposed model, outperforms our previous work, showing that additional clinical data improved the accuracy and reduced the number of “expert” type diagnostic outputs.
Conclusions:
The tool using AI is safe and effective in identifying people who may have ADHD using routine clinical data hence speeding up the diagnostic process. Further research can focus on the acceptability of using this technology.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.