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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