Finding descriptions of PPMI features

jf.daneault
Sun, 06 Oct 2024 04:40:30 +0000
Accessing and Understanding Data

Hi,
my student and I are looking to leverage machine learning models and the PPMI dataset to examine the progression of various symptoms. As a first step, we linked each feature to its description in the data dictionary. However, we ran into a problem. We found features in the data that are not in the dictionary so we don’t know what they mean.
Here is the list of features we could not find: ‘MCAALTTM’, ‘MCACUBE’, ‘MCACLCKC’, ‘MCACLCKN’, ‘MCACLCKH’, ‘MCALION’, ‘MCARHINO’, ‘MCACAMEL’, ‘MCAFDS’, ‘MCABDS’, ‘MCASNTNC’, ‘MCAABSTR’, ‘MCAREC1’, ‘MCAREC2’, ‘MCAREC3’, ‘MCAREC4’, ‘MCAREC5’, ‘MCADATE’, ‘MCAMONTH’, ‘MCAYR’, ‘MCADAY’, ‘MCAPLACE’, ‘MCACITY’, ‘CLCKPII’, ‘CLCK2HND’, ‘CLCKNMRK’, ‘CLCKNUIN’, ‘CLCKALNU’, ‘CLCKNUSP’, ‘CLCKNUED’, ‘LNS1A’, ‘LNS1B’, ‘LNS1C’, ‘LNS2A’, ‘LNS2B’, ‘LNS2C’, ‘LNS3A’, ‘LNS3B’, ‘LNS3C’, ‘LNS4A’, ‘LNS4B’, ‘LNS4C’, ‘LNS5A’, ‘LNS5B’, ‘LNS5C’, ‘LNS6A’, ‘LNS6B’, ‘LNS6C’.
They seem to be individual items of certain tests (e.g., MCACUBE may be the cube drawing item from the MoCA) and they are all categorical.
Does anyone know where we could find the definition for these features?
Another question I would like to ask is: if those are in fact individual items of clinical scales, should we include them in our machine learning models since the scales are usually validated as a whole, not on an individual item basis?

6 posts - 3 participants