NLP with Python with a Cognitive database There are 4 cognitive status cases (normal, impaired not MCI, MCI, dementia). Dataset used in one of my publications: "Narrative video scene description task discriminates between levels of cognitive impairment in Alzheimer’s disease." https://psycnet.apa.org/record/2020-05494-001
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Notebook to check what can be done using NLP (TF-IDF approach) I predicted the cognitive status by using number of words in the descriptions and also apply a NLP approach that examines the vocabulary using the TF-IDF metric.
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Notebook to calculate new metric comparable to the information acquisition score and prediction using the new model using Random Forest classifier. The new model is based on 4 features:
Matches with the top words (N=28) for that clip;
Number of words not mentioned in any of the descriptions for that clip;
Length of the description;
Averaged speech rate;