multiple sclerosis about pysustain

[noxtoby]

Quick answer here until someone else has time to followup:

• later in this tutorial notebook you can find patient staging and subtyping, which is how you "score" your patients (using the same input features as in the pickled/pre-trained model)
• then you could perform an association analysis of outcomes as a function of subtype and/or stage

[luantunez]

Thank you very much for your help and quick response!
I have read the pkl file and I am getting the following content:

The problem is that I do not fully understand them and wouldn´t know how to set them for inference of MS on a new MRI volume.
Could you guide me on that please?

{'ml_sequence': array([[[ 7.],
        [11.],
        [ 6.],
        [10.],
        [ 9.],
        [ 3.],
        [20.],
        [ 8.],
        [22.],
        [19.],
        [21.],
        [23.],
        [24.],
        [33.],
        [ 4.],
        [35.],
        [12.],
        [34.],
        [32.],
        [ 2.],
        [36.],
        [16.],
        [17.],
        [37.],
        [ 1.],
        [15.],
        [14.],
        [ 0.],
        [13.],
        [25.],
        [38.],
        [28.],
        [30.],
        [26.],
        [27.],
        [29.],
        [ 5.],
        [18.],
        [31.]],

       [[ 3.],
        [ 4.],
        [ 0.],
        [12.],
        [25.],
        [38.],
        [13.],
        [ 5.],
        [ 1.],
        [ 2.],
        [17.],
        [15.],
        [16.],
        [14.],
        [11.],
        [18.],
        [ 7.],
        [20.],
        [10.],
        [24.],
        [ 6.],
        [ 9.],
        [ 8.],
        [22.],
        [21.],
        [23.],
        [19.],
        [26.],
        [28.],
        [30.],
        [27.],
        [31.],
        [29.],
        [33.],
        [35.],
        [34.],
        [36.],
        [37.],
        [32.]],

       [[ 3.],
        [ 4.],
        [ 1.],
        [ 2.],
        [ 0.],
        [ 5.],
        [12.],
        [11.],
        [17.],
        [ 7.],
        [16.],
        [10.],
        [20.],
        [ 9.],
        [ 6.],
        [15.],
        [ 8.],
        [22.],
        [19.],
        [21.],
        [23.],
        [24.],
        [14.],
        [25.],
        [38.],
        [13.],
        [33.],
        [35.],
        [34.],
        [32.],
        [36.],
        [37.],
        [30.],
        [28.],
        [18.],
        [31.],
        [26.],
        [29.],
        [27.]]]), 'ml_f': array([[0.34185156],
       [0.25388692],
       [0.40426152]]), 'ml_likelihood': array([-137458.80978643]), 'samples_sequence': array([[[ 7.,  7.,  7., ...,  7.,  7.,  7.],
        [11., 11., 11., ..., 11., 11., 11.],
        [ 6.,  6.,  6., ...,  6.,  6.,  6.],
        ...,
        [ 5.,  5.,  5., ..., 18., 18., 18.],
        [18., 18., 18., ..., 31., 31., 31.],
        [31., 31., 31., ..., 30., 30., 30.]],

       [[ 3.,  3.,  3., ...,  3.,  3.,  3.],
        [ 4.,  4.,  4., ...,  4.,  4.,  4.],
        [ 0.,  0.,  0., ...,  0.,  0.,  0.],
        ...,
        [36., 36., 36., ..., 36., 36., 36.],
        [37., 37., 37., ..., 37., 37., 37.],
        [32., 32., 32., ..., 32., 32., 32.]],

       [[ 3.,  3.,  3., ...,  3.,  3.,  3.],
        [ 4.,  4.,  4., ...,  4.,  4.,  4.],
        [ 1.,  1.,  1., ...,  1.,  1.,  1.],
        ...,
        [26., 26., 26., ..., 31., 31., 31.],
        [29., 29., 29., ..., 29., 29., 29.],
        [27., 27., 27., ..., 27., 27., 27.]]]), 'samples_f': array([[0.34028093, 0.3422053 , 0.3422053 , ..., 0.34814804, 0.34880428,
        0.35054254],
       [0.25384016, 0.25389745, 0.25389745, ..., 0.2540744 , 0.25409394,
        0.2541457 ],
       [0.40587892, 0.40389725, 0.40389725, ..., 0.39777756, 0.39710178,
        0.39531176]]), 'samples_likelihood': array([[-137460.9034704 ],
       [-137460.95568907],
       [-137460.95568907],
       ...,
       [-137468.27126633],
       [-137468.40862701],
       [-137468.82692007]])}

[noxtoby]

Did you look at the tutorial notebook?