To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
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.
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.
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).
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.
Email your librarian or administrator to recommend adding this to your organisation's collection.