UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier

Tolonen, A; Rhodius-Meester, HFM; Bruun, M; Koikkalainen, J; Barkhof, F; Lemstra, AW; Koene, T; ... Lotjonen, J; + view all (2018) Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier. Frontiers in Aging Neuroscience , 10 , Article 111. 10.3389/fnagi.2018.00111. Green open access

[thumbnail of Barkhof_fnagi-10-00111.pdf]
Preview
Text
Barkhof_fnagi-10-00111.pdf - Published Version

Download (998kB) | Preview

Abstract

Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer’s disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

Type: Article
Title: Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fnagi.2018.00111
Publisher version: https://doi.org/10.3389/fnagi.2018.00111
Language: English
Additional information: © 2018 Tolonen, Rhodius-Meester, Bruun, Koikkalainen, Barkhof, Lemstra, Koene, Scheltens, Teunissen, Tong, Guerrero, Schuh, Ledig, Baroni, Rueckert, Soininen, Remes, Waldemar, Hasselbalch, Mecocci, van der Flier and Lötjönen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Science & Technology, Life Sciences & Biomedicine, Geriatrics & Gerontology, Neurosciences, Neurosciences & Neurology, Neurodegenerative Diseases, Classification, Decision Support, Alzheimer's Disease, Frontotemporal Lobar Degeneration, Vascular Dementia, Dementia With Lewy Bodies, Mild Cognitive Impairment, Early Alzheimers-Disease, Temporal-Lobe Atrophy, Frontotemporal Dementia, Lewy Bodies, Behavioral Variant, Intraobserver Reproducibility, Neuropsychological Tests, International Workshop, Clinical-Diagnosis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
URI: https://discovery.ucl.ac.uk/id/eprint/10049133
Downloads since deposit
67Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item