Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers

• URL: http://arxiv.org/abs/2012.05492v1
• Date: Thu, 10 Dec 2020 07:33:59 GMT
• Title: Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers
• Authors: Jeremy Levy, Daniel Alvarez, Felix del Campo, and Joachim A. Behar
• Abstract summary: COPD is a major source of morbidity, mortality and healthcare costs. No research has looked at the feasibility of COPD diagnosis from the nocturnal oximetry time series. We introduce a novel approach to nocturnal COPD diagnosis using 44 oximetry digital biomarkers and 5 demographic features.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Objective: Chronic obstructive pulmonary disease (COPD) is a highly prevalent chronic condition. COPD is a major source of morbidity, mortality and healthcare costs. Spirometry is the gold standard test for a definitive diagnosis and severity grading of COPD. However, a large proportion of individuals with COPD are undiagnosed and untreated. Given the high prevalence of COPD and its clinical importance, it is critical to develop new algorithms to identify undiagnosed COPD, especially in specific groups at risk, such as those with sleep disorder breathing. To our knowledge, no research has looked at the feasibility of COPD diagnosis from the nocturnal oximetry time series. Approach: We hypothesize that patients with COPD will exert certain patterns and/or dynamics of their overnight oximetry time series that are unique to this condition. We introduce a novel approach to nocturnal COPD diagnosis using 44 oximetry digital biomarkers and 5 demographic features and assess its performance in a population sample at risk of sleep-disordered breathing. A total of n=350 unique patients polysomnography (PSG) recordings. A random forest (RF) classifier is trained using these features and evaluated using the nested cross-validation procedure. Significance: Our research makes a number of novel scientific contributions. First, we demonstrated for the first time, the feasibility of COPD diagnosis from nocturnal oximetry time series in a population sample at risk of sleep disordered breathing. We highlighted what digital oximetry biomarkers best reflect how COPD manifests overnight. The results motivate that overnight single channel oximetry is a valuable pathway for COPD diagnosis.

Related papers

• Prediction of COPD Using Machine Learning, Clinical Summary Notes, and Vital Signs [0.0]
  Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. This paper presents two different predictive models to predict COPD exacerbation using AI and natural language processing (NLP) approaches. We achieved an area under the Receiver operating characteristic (ROC) curve of 0.82 in detection and prediction of COPD exacerbation.
  arXiv  Detail & Related papers  (2024-08-25T23:41:39Z)
• Revisiting Computer-Aided Tuberculosis Diagnosis [56.80999479735375]
  Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. We establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD.
  arXiv  Detail & Related papers  (2023-07-06T08:27:48Z)
• Fractional dynamics foster deep learning of COPD stage prediction [2.6414930652238535]
  Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. We address COPD detection by constructing two novel physiological signals datasets.
  arXiv  Detail & Related papers  (2023-03-13T23:47:46Z)
• Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies [1.234198411367205]
  Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world and the third leading cause of mortality worldwide. It is often underdiagnosed or not diagnosed until later in the disease course. Spirometry tests are the gold standard for diagnosing COPD but can be difficult to obtain, especially in resource-poor countries. Chest X-rays (CXRs) are readily available and may serve as a screening tool to identify patients with COPD who should undergo further testing.
  arXiv  Detail & Related papers  (2022-11-13T15:12:22Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• Intelligent Sight and Sound: A Chronic Cancer Pain Dataset [74.77784420691937]
  This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial. The data collected to date consists of 29 patients, 509 smartphone videos, 189,999 frames, and self-reported affective and activity pain scores. Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain.
  arXiv  Detail & Related papers  (2022-04-07T22:14:37Z)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach [67.05664774727208]
  Chest radiography is an alternative screening method for the COVID-19. Computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed. To address this challenge, we propose the use of a low-shot learning approach named imprinted weights.
  arXiv  Detail & Related papers  (2021-05-04T19:01:40Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals [0.0]
  DAS-KSVD was applied to detect and classify apnea and hypopnea events from signals obtained from the Sleep Heart Health Study database. For moderate to severe OSAH screening, a receiver operating characteristic curve analysis of the results shows an area under the curve of 0.957 and diagnostic sensitivity and specificity of 87.56% and 88.32%, respectively.
  arXiv  Detail & Related papers  (2020-03-22T15:17:20Z)
• COPD Classification in CT Images Using a 3D Convolutional Neural Network [10.217631381481457]
  Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. We propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.
  arXiv  Detail & Related papers  (2020-01-04T16:58:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.