A DEEP LEARNING FRAMEWORK FOR CLASSIFICATION OF SEVERITY IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD)

2018 ◽  
Author(s):  
Roger Tam
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Deep Learning ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Learning Framework

scholarly journals A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes

2019 ◽  
Vol 19 (S8) ◽  
Author(s):  
Chunlei Tang ◽  
Joseph M. Plasek ◽  
Haohan Zhang ◽  
Min-Jeoung Kang ◽  
Haokai Sheng ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Deep Learning ◽  
Disease Progression ◽  
Pulmonary Disease ◽  
Time Lapse ◽  
Chronic Obstructive ◽  
Allocation Model ◽  
Obstructive Pulmonary Disease ◽  
Clinical Notes ◽  
Copd Patients

Abstract Background Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We aimed to characterize the time to progression prior to death in patients with COPD and to generate a temporal visualization that describes signs and symptoms during different stages of COPD progression. Methods We present a two-step approach for visualizing COPD progression at the level of unstructured clinical notes. We included 15,500 COPD patients who both received care within Partners Healthcare’s network and died between 2011 and 2017. We first propose a four-layer deep learning model that utilizes a specially configured recurrent neural network to capture irregular time lapse segments. Using those irregular time lapse segments, we created a temporal visualization (the COPD atlas) to demonstrate COPD progression, which consisted of representative sentences at each time window prior to death based on a fraction of theme words produced by a latent Dirichlet allocation model. We evaluated our approach on an annotated corpus of COPD patients’ unstructured pulmonary, radiology, and cardiology notes. Results Experiments compared to the baselines showed that our proposed approach improved interpretability as well as the accuracy of estimating COPD progression. Conclusions Our experiments demonstrated that the proposed deep-learning approach to handling temporal variation in COPD progression is feasible and can be used to generate a graphical representation of disease progression using information extracted from clinical notes.

scholarly journals How to Stage Airflow Limitation in Stable Chronic Obstructive Pulmonary Disease Male Patients?

2020 ◽  
Vol 14 (3) ◽  
pp. 155798832092263
Author(s):  
Ichraf Anane ◽  
Fatma Guezguez ◽  
Hend Knaz ◽  
Helmi Ben Saad
Keyword(s):  
Quality Of Life ◽  
Chronic Obstructive Pulmonary Disease ◽  
Health Outcomes ◽  
Pulmonary Disease ◽  
Airflow Limitation ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Male Patients

No study has evaluated the utility of different classifications of chronic obstructive pulmonary disease (COPD) airflow limitation (AFL) in terms of the refined “ABCD” classification of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) or in terms of the impacts on quality of life. This study aimed to compare some relevant health outcomes (i.e., GOLD classification and quality-of-life scores) between COPD patients having “light” and “severe” AFL according to five COPD AFL classifications. It was a cross-sectional prospective study including 55 stable COPD male patients. The COPD assessment test (CAT), the VQ11 quality-of-life questionnaire, a spirometry, and a bronchodilator test were performed. The patients were divided into GOLD “A/B” and “C/D.” The following five classifications of AFL severity, based on different post-bronchodilator forced expiratory volume in 1 s (FEV1) expressions, were applied: FEV1%pred: “light” (≥50), “severe” (<50); FEV1z-score: “light” (≥−3), “severe” (<−3); FEV1/height2: “light” (≥0.40), “severe” (<0.40); FEV1/height3: “light” (≥0.29), “severe” (<0.29); and FEV1Quotient: “light” (≥2.50), “severe” (<2.50). The percentages of the patients with “severe” AFL were significantly influenced by the applied classification of the AFL severity (89.1 [FEV1z-score], 63.6 [FEV1%pred], 41.8 [FEV1/height3], 40.0 [FEV1Quotient], and 25.4 [FEV1/height2]; Cochrane test = 91.49, df = 4). The CAT and VQ11 scores were significantly different between the patients having “light” and “severe” AFL. In GOLD “C/D” patients, only the FEV1Quotient was able to distinguish between the two AFL severities. To conclude, the five classifications of COPD AFL were not similar when compared with regard to some relevant health outcomes.

scholarly journals A Study on Diagnostic Assist Systems of Chronic Obstructive Pulmonary Disease from Medical Images by Deep Learning

2018 ◽  
Vol 06 (01) ◽  
pp. 21-31 ◽  
Author(s):  
Toru Kimura ◽  
Takashi Kawakami ◽  
Akihiro Kikuchi ◽  
Ryosuke Ooev ◽  
Masaki Akiyama ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Deep Learning ◽  
Pulmonary Disease ◽  
Medical Images ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease

scholarly journals Cutaneous Nocardia asteroides infection of nontraumatic origin

1994 ◽  
Vol 36 (5) ◽  
pp. 403-408 ◽  
Author(s):  
Jorge O Lopes ◽  
Maria C Bassanesi ◽  
Sydney H Alves ◽  
Adenilde Salla ◽  
Jeni P Benevenga ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Histological Examination ◽  
Biopsy Specimen ◽  
Disease Diagnosis ◽  
Nocardia Asteroides ◽  
Chronic Obstructive ◽  
Cutaneous Infection ◽  
Obstructive Pulmonary Disease

This paper reports a case of cutaneous infection of nontraumatic origin caused by Nocardia asteroides in a hospitalized patient with chronic obstructive pulmonary disease. Diagnosis was established by direct and histological examination, cultures from exudate and biopsy specimen. We discuss the classification of clinical forms of Nocardia infections affecting the skin.

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