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A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680466/ https://www.ncbi.nlm.nih.gov/pubmed/33235709 http://dx.doi.org/10.1007/s13755-020-00123-7 |
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author | Tachmazidis, Ilias Chen, Tianhua Adamou, Marios Antoniou, Grigoris |
author_facet | Tachmazidis, Ilias Chen, Tianhua Adamou, Marios Antoniou, Grigoris |
author_sort | Tachmazidis, Ilias |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7680466 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-76804662020-11-23 A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults Tachmazidis, Ilias Chen, Tianhua Adamou, Marios Antoniou, Grigoris Health Inf Sci Syst Research 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. Springer International Publishing 2020-11-20 /pmc/articles/PMC7680466/ /pubmed/33235709 http://dx.doi.org/10.1007/s13755-020-00123-7 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Tachmazidis, Ilias Chen, Tianhua Adamou, Marios Antoniou, Grigoris A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
title | A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
title_full | A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
title_fullStr | A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
title_full_unstemmed | A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
title_short | A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
title_sort | hybrid ai approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (adhd) in adults |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680466/ https://www.ncbi.nlm.nih.gov/pubmed/33235709 http://dx.doi.org/10.1007/s13755-020-00123-7 |
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