Subtypes in ALZ, model for PD

[aspergerian]

The subtypes model sketched in the synopsis seems also applicable

for PD.

New data mining strategy spots those at high risk of Alzheimer's

sciencedaily.com/releases/2...

[aspergerian]

Quotes from Gamberger et al 2017.

"Unsupervised cluster analysis is a data mining method that is increasingly used across many diverse fields to

unearth new insights in multidimensional data. It does not require explicit assumptions about the target variables.

Such methods may also offer insights into AD given the variability in clinical outcomes and prior autopsy literature

noting the existence of patient clusters with unique pathological phenotypes (such as a subgroup with very

localized cortical distribution of senile plaques versus another subgroup with more widely distributed plaques)16.

To our knowledge, such clustering algorithms have not been previously applied to the study of longitudinal

changes in people at risk for AD."

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"Progression from MCI to Dementia in Slow versus Rapid Subpopulations. We examined the categorical

diagnostic changes from MCI to dementia as determined by the clinician. At each visit, the diagnosis of

the subject as reassessed by the site physician using all available information and the clinician rated whether the

subject was stable, had converted to dementia or reverted to cognitively normal status. For the complete MCI

population the rate of conversion to dementia from MCI was 42% and the rate of reversion from MCI to normal

was 4%. The rate of conversion to dementia from MCI was 64% in the rapid cluster and 13% in the slow cluster.

In the rapid cluster there was no reversion from MCI to normal while in the slow cluster it has been 10%. These

differences were statistically significant (p < 0.001)."

nature.com/articles/s41598-...

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Exploratory Clustering for Patient Subpopulation Discovery.

researchgate.net/publicatio...

Exploratory Clustering is a novel general purpose clustering tool which

is especially appropriate for medical domains in which we need to

identify subpopulations that are similar in two different data layers.

The tool implements the multi-layer clustering algorithm in a framework

that enables iterative experiments by the user in his search for

relevant patient subpopulations. A unique property of the tool is

integration of clustering and feature selection algorithms. Differences

in values of most relevant attributes are used to demonstrate decisive

properties of constructed clusters. Usefulness of the tool is

illustrated on a task of discovering groups of patients with similar

cognitive impairment. © 2017 European Federation for Medical Informatics

(EFMI) and IOS Press.

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Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre OPDC Discovery Cohort.

Lawton M et al J Parkinsons Dis (2015).

ncbi.nlm.nih.gov/pubmed/264...