An artificial intelligence approach to early predict symptom-based exacerbations of COPD

Figures & data

Figure 1. Sub-bands used in the discrete wavelet transform implementation (marked in green colour).

Figure 2. Receiver operating characteristic curve for the validated DTF classifier.

Figure 3. Flowchart with complete information on patient involvement, dropout and AECOPD predicted during the pilot study using a decision tree forest. Symptom-based exacerbations were considered.

Table 1. Classifier performance evaluation.

Indicator	Value
Total AECOPD predicted	32 (78.0%)
AECOPD reported predicted	25 (75.8%)
AECOPD unreported predicted	7 (87.5%)
True positives	35.6%
False positives	2.2%
True negatives	52.2%
False negatives	10.0%
Accuracy	87.8%
Sensitivity	78.1%
Specificity	95.9%
Positive predictive value	94.1%
Negative predictive value	83.9%
False alarms of the system	2
F1 score	0.8
Matthews correlation coefficient	0.8

Figure 4. Histogram of prediction margins of total, reported and unreported AECOPD. The horizontal axis indicates the days of prediction prior to AECOPD onset.