| Frontiers in Aging Neuroscience | |
| Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier | |
| Wiesje M. van der Flier1 Teddy Koene2 Philip Scheltens2 Charlotte E. Teunissen2 Afina W. Lemstra2 Hanneke F. M. Rhodius-Meester2 Jyrki Lötjönen3 Juha Koikkalainen3 Marie Bruun4 Steen G. Hasselbalch4 Gunhild Waldemar4 Daniel Rueckert5 Christian Ledig5 Tong Tong5 Ricardo Guerrero5 Andreas Schuh5 Patrizia Mecocci7 Marta Baroni7 Frederik Barkhof8 Anne M. Remes9 Hilkka Soininen9 Antti Tolonen1,10 | |
| [1] 0Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, Netherlands;Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands;Combinostics Ltd., Tampere, Finland;Danish Dementia Research Centre, Rigshospitalet, Copenhagen, Denmark;Imperial College London, London, United Kingdom;Institute of Clinical Medicine and Department of Neurology, University of Eastern Finland, Kuopio, Finland;Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy;Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom;Neurology, Neurocenter, Kuopio University Hospital, Kuopio, Finland;VTT Technical Research Centre of Finland, Tampere, Finland; | |
| 关键词: neurodegenerative diseases; classification; decision support; Alzheimer’s disease; frontotemporal lobar degeneration; vascular dementia; | |
| DOI : 10.3389/fnagi.2018.00111 | |
| 来源: DOAJ | |
【 摘 要 】
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.
【 授权许可】
Unknown