TY - JOUR

T1 - Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence

T2 - a clinical study in the UK

AU - Chen, Tianhua

AU - Tachmazidis, Ilias

AU - Batsakis, Sotiris

AU - Adamou, Marios

AU - Papadakis, Emmanuel

AU - Antoniou, Grigoris

N1 - Funding Information: This research was partially supported by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor with Grant No. 01DD20003.

PY - 2023/6/9

Y1 - 2023/6/9

N2 - Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learning model and a knowledge-based model) for assisting adult ADHD diagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future.

AB - Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learning model and a knowledge-based model) for assisting adult ADHD diagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future.

KW - artificial intelligence

KW - attention-deficit hyperactivity disorder (ADHD)

KW - diagnostic system

KW - explainable AI

KW - machine learning

KW - mental health

UR - http://www.scopus.com/inward/record.url?scp=85163683773&partnerID=8YFLogxK

U2 - 10.3389/fpsyt.2023.1164433

DO - 10.3389/fpsyt.2023.1164433

M3 - Article

AN - SCOPUS:85163683773

VL - 14

JO - Frontiers in psychiatry

JF - Frontiers in psychiatry

SN - 1664-0640

M1 - 1164433

ER -