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

a clinical study in the UK

verfasst von

Tianhua Chen, Ilias Tachmazidis, Sotiris Batsakis, Marios Adamou, Emmanuel Papadakis, Grigoris Antoniou

Abstract

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

University of Huddersfield
Technical University of Crete
South West Yorkshire Partnership NHS Foundation Trust

Typ

Artikel

Journal

Frontiers in psychiatry

Band

14

ISSN

1664-0640

Publikationsdatum

09.06.2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Psychiatrie und psychische Gesundheit

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.3389/fpsyt.2023.1164433 (Zugang: Offen)