Estimating Time to Progression of Chronic Obstructive Pulmonary Disease with Tolerance


Tang C. (first author). 1/2/2021. “Estimating Time to Progression of Chronic Obstructive Pulmonary Disease with Tolerance.” IEEE Journal of Biomedical and Health Informatics, 1, 25, Pp. 175-180. Publisher's Version


This paper proposes a tolerance range of the upper and lower boundaries of a preset time segment for the basic machine learning algorithms such as linear regression (LR) and support vector machines (SVMs) and investigates improvement rate (IR) on the accuracy in predicting mortality risk in patients on a corpus of clinical notes. The corpus includes pulmonary, cardiology, and radiology reports of 15,500 patients with chronic obstructive pulmonary disease who died between 2011 and 2017. Their performance is compared against a state-of-the-art long short-term memory recurrent neural network model. The results demonstrate an overall improvement by machine learning approaches when considering an optimal tolerance range: the average IR of LR is 90.1% and the maximum IR of SVMs is 66.2%. We achieved very similar results to deep learning. In addition, this paper contrasts two temporal visualizations on pulmonary notes, which consisted of representative sentences at each time segment prior to death based on a fraction of theme words produced by a latent Dirichlet allocation model.
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Last updated on 01/07/2021