Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 24, Issue 6, Pages 1805-1813Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2016.2642944
Keywords
Diseases; Hospitals; Machine learning; Lungs; Electronic mail; Gold; Feature extraction; Chronic obstructive pulmonary disease (COPD); deep belief network (DBN); deep learning; exacerbation; fisher score
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Funding
- National Institutes of Health (NIH) [R01EB013293, K01AG050711]
- NIH [R01HL089897, R01HL089856]
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL089856, R01HL089897] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [K01AG050711] Funding Source: NIH RePORTER
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This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. A total of 10 300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a ten-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We, thus, demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.
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