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Table 4 Univariate analysis on respiratory diagnosis between CC and non-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

Chronic airway obstruction

82 (0.95%)

1376 (29.54%)

318 (18.31%)

25 (16.23%)

< .0001

Obstructive chronic bronchitis

41 (0.48%)

741 (15.91%)

178 (10.25%)

17 (11.04%)

< .0001

Cough

323 (3.76%)

2503 (53.74%)

1001 (57.63%)

79 (51.3%)

< .0001

Pneumonia

99 (1.15%)

1143 (24.54%)

279 (16.06%)

13 (8.44%)

< .0001

Shortness of breath

121 (1.41%)

1156 (24.82%)

323 (18.6%)

23 (14.94%)

< .0001

Other dyspnea

148 (1.72%)

1296 (27.82%)

295 (16.98%)

24 (15.58%)

< .0001

Asthma

230 (2.68%)

716 (15.37%)

284 (16.35%)

21 (13.64%)

< .0001

Other diseases of lung

55 (0.64%)

559 (12%)

173 (9.96%)

7 (4.55%)

< .0001

Acute bronchitis and bronchiolitis

99 (1.15%)

597 (12.82%)

248 (14.28%)

19 (12.34%)

< .0001

Respiratory Failure

18 (0.21%)

568 (12.19%)

86 (4.95%)

3 (1.95%)

< .0001

Pleurisy pleural effusion

27 (0.31%)

533 (11.44%)

102 (5.87%)

6 (3.9%)

< .0001