Skip to main content

Table 1 Dataset. For each feature, the type either static (S) or dynamic (D) is defined. For the continuous and ordinal features, percentage of native missing values and inter-quartile range (IQR) values at 25%, 50% and 75% are reported; for the categorical features, levels and corresponding percentage of instances are reported; for the NIV and PEG variables, we reported the total number of patients who were administered these interventions

From: Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach

Continuous features

 

Categorical features

Feature

Type

% NA

IQR

 

Feature

Type

Levels

%

BMI premorbid [kg/m2]

S

2.08

23/25/28

 

sex

S

Female

47.6

BMI diagnosis [kg/m2]

S

0.91

22/24/27

   

Male

52.4

FVC diagnosis [%]

S

4.12

83/98/108

   

NA

0

age at onset [years]

S

0

56/64/70

 

familiality

S

No

91.4

diagnostic delay [months]

S

0

5/9/14

   

Yes

8.1

onset delta [months]

S

0

-18/-11/-6

   

NA

0.5

     

genetics

S

C9orf72

7.1

       

FUS

0.3

       

SOD1

1.4

       

TARDBP

1.6

Ordinal features

   

wild type

83.6

Feature

Type

% NA

IQR

   

NA

6.0

ALSFRS-R 1

D

0

2/3/4

 

FTD

S

No

53.0

ALSFRS-R 2

D

0

3/4/4

   

Yes

13.0

ALSFRS-R 3

D

0

2/3/4

   

NA

34.0

ALSFRS-R 4

D

0

2/3/4

 

onset site

S

Bulbar

34.4

ALSFRS-R 5

D

0

1/2/3

   

Limb

65.6

ALSFRS-R 6

D

0

1/2/3

   

NA

0

ALSFRS-R 7

D

0

1/3/3

 

NIV

D

No

59.6

ALSFRS-R 8

D

0

2/2/3

   

Yes

40.4

ALSFRS-R 9

D

0

0/1/3

   

NA

0

ALSFRS-R 10

D

0

3/4/4

 

PEG

D

No

31.9

ALSFRS-R 11

D

0

3/4/4

   

Yes

25.0

ALSFRS-R 12

D

0

4/4/4

   

NA

43.1