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Table 3  Univariate analysis of the categorized diagnosis, medication, and lab data of the non-CC and CC clusters

From: Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data

 

Non-CC (N = 8588)

CC-1 (N = 4658)

CC-2 (N = 1737)

CC-3 (N = 154)

p value

Diagnosis

Respiratory

2003 (23.32%)

4300 (92.31%)

1593 (91.71%)

140 (90.91%)

< .0001

Endocrine metabolic

1268 (14.76%)

3514 (75.44%)

1026 (59.07%)

88 (57.14%)

< .0001

Circulatory system

1687 (19.64%)

3735 (80.18%)

1110 (63.9%)

91 (59.09%)

< .0001

Mental disorder

1020 (11.88%)

3133 (67.26%)

857 (49.34%)

56 (36.36%)

< .0001

Neurological

805 (9.37%)

2381 (51.12%)

579 (33.33%)

38 (24.68%)

< .0001

Digestive

1419 (16.52%)

2889 (62.02%)

767 (44.16%)

60 (38.96%)

< .0001

Symptoms

2344 (27.29%)

3335 (71.6%)

943 (54.29%)

71 (46.1%)

< .0001

Hematopoietic

336 (3.91%)

2353 (50.52%)

484 (27.86%)

20 (12.99%)

< .0001

Medication

Antiasthmatic bronchodilator

1099 (12.8%)

2777 (59.62%)

898 (51.7%)

79 (51.3%)

< .0001

Minerals electrolytes

1164 (13.55%)

2791 (59.92%)

704 (40.53%)

52 (33.77%)

< .0001

Corticosteroids

1035 (12.05%)

2529 (54.29%)

728 (41.91%)

61 (39.61%)

< .0001

Ulcer drugs

1714 (19.96%)

2789 (59.88%)

769 (44.27%)

55 (35.71%)

< .0001

Lab

Blood count

3437 (40.02%)

4078 (87.55%)

1287 (74.09%)

105 (68.18%)

< .0001

  1. The p values for the comparison are bracketed in the last column. Only < .0001 was listed in the last column if all p values were < .0001