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A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion: further evidence of its accuracy via a transfer learning approach

Published online by Cambridge University Press:  14 November 2018

Massimiliano Grassi Open the ORCID record for Massimiliano Grassi [Opens in a new window] ,
David A. Loewenstein ,
Daniela Caldirola ,
Koen Schruers ,
Ranjan Duara  and
Giampaolo Perna
Massimiliano Grassi*
Affiliation:
Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy
David A. Loewenstein
Affiliation:
Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida, USA Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida, USA Center on Aging, Miller School of Medicine, University of Miami, Miami, Florida, USA
Daniela Caldirola
Affiliation:
Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy
Koen Schruers
Affiliation:
Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, University of Maastricht, Maastricht, the Netherlands
Ranjan Duara
Affiliation:
Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida, USA Courtesy Professor of Neurology, Department of Neurology, University of Florida College of Medicine, Gainesville, Florida, USA Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, USA
Giampaolo Perna
Affiliation:
Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida, USA Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, University of Maastricht, Maastricht, the Netherlands Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy Mantovani Foundation, Arconate, Italy
*
Correspondence should be addressed to: Massimiliano Grassi, Department of Clinical Neurosciences, Villa San Benedetto Menni, Hermanas Hospitalarias, FoRiPsi, via Roma 16, 22032 Albese con Cassano (Como), Italy. Phone: +39 031 4291511; Fax: +39 031 427246. Email: massi.gra@gmail.com.
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Abstract

Background:

In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer’s disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach.

Methods:

We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study.

Results:

Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705–0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706).

Conclusions:

These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.

Keywords

Alzheimer’s diseaseclinical prediction rulemachine learningmild cognitive impairmentpersonalized medicineprecision medicinetransfer learning
Type
Original Research Article
Information
International Psychogeriatrics , Volume 31 , Issue 7: Issue Theme: Diverse Approaches to Stemming Cognitive Decline , July 2019 , pp. 937 - 945
Copyright
© International Psychogeriatric Association 2018 

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