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

Antti Tolonen (Corresponding Author), Hanneke F.M. Rhodius-Meester, Marie Bruun, Juha Koikkalainen, Frederik Barkhof, Afina W. Lemstra, Teddy Koene, Philip Scheltens, Charlotte Elisabeth Teunissen, Tong Tong, Ricardo Guerrero, Andreas Schuh, Christian Ledig, Marta Baroni, Daniel Rueckert, Hilkka Soininen, Anne M. Remes, Gunhild Waldermar, Steen G. Hasselbach, Patrizia MecocciWiesje van der Flier, Jyrki Lötjönen

    Research output: Contribution to journalArticleScientificpeer-review

    25 Citations (Scopus)


    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.
    Original languageEnglish
    Article number111
    Number of pages11
    JournalFrontiers in Aging Neuroscience
    Publication statusPublished - 25 Apr 2018
    MoE publication typeA1 Journal article-refereed


    This work received funding from the European Union’s Seventh Framework Program for Research, Technological Development and Demonstration under grant agreement nos. 611005 (PredictND) and 601055 (VPH-DARE@IT). The VUmc Alzheimer Center is supported by Alzheimer Nederland and Stichting VUmc funds. The clinical database structure of the VUmc Alzheimer Center was developed with funding from Stichting Dioraphte. FB was supported by the NIHR UCLH Biomedical Research Centre.


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