A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults

• Published in: Health information science and systems Vol. 9; no. 1; p. 1
• Main Authors: Tachmazidis, Ilias, Chen, Tianhua, Adamou, Marios, Antoniou, Grigoris
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2021; BioMed Central Ltd; Springer Nature B.V
• Subjects: Adults; Algorithms; Attention deficit hyperactivity disorder; Bioinformatics; Computational Biology/Bioinformatics; Computer Science; Data analysis; Decision-making; Diagnosis; Health Informatics; Hyperactivity; Information Systems and Communication Service; Knowledge engineering; Machine learning; Medical research; Medicine, Experimental; Mental health; Neurodevelopmental disorders; Psychiatric services; Public health; Signs and symptoms; Knowledge model; ADHD diagnosis; Machine learning model; AI in medicine; Decision making support
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-020-00123-7

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that includes symptoms such as inattentiveness, hyperactivity and impulsiveness. It is considered as an important public health issue and prevalence of, as well as demand for diagnosis, has increased as awareness of the disease grew over the past years. Supply of specialist medical experts has not kept pace with the increasing demand for assessment, both due to financial pressures on health systems and the difficulty to train new experts, resulting in growing waiting lists. Patients are not being treated quickly enough causing problems in other areas of health systems (e.g. increased GP visits, increased risk of self-harm and accidents) and more broadly (e.g. time off work, relationship problems). Advances in AI make it possible to support the clinical diagnosis of ADHD based on the analysis of relevant data. This paper reports on findings related to the mental health services of a specialist Trust within the UK’s National Health Service (NHS). The analysis studied data of adult patients who underwent diagnosis over the past few years, and developed a hybrid approach, consisting of two different models: a machine learning model obtained by training on data of past cases; and a knowledge model capturing the expertise of medical experts through knowledge engineering. The resulting algorithm has an accuracy of 95% on data currently available, and is currently being tested in a clinical environment.