Prediction of COPD Using Machine Learning, Clinical Summary Notes, and Vital Signs

• URL: http://arxiv.org/abs/2408.13958v2
• Date: Thu, 5 Sep 2024 02:07:11 GMT
• Title: Prediction of COPD Using Machine Learning, Clinical Summary Notes, and Vital Signs
• Authors: Negar Orangi-Fard,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. In the United States, more than 15.7 million Americans have been diagnosed with COPD, with 96% of individuals living with at least one other chronic health condition. It is the 4th leading cause of death in the country. Over 2.2 million patients are admitted to hospitals annually due to COPD exacerbations. Monitoring and predicting patient exacerbations on-time could save their life. This paper presents two different predictive models to predict COPD exacerbation using AI and natural language processing (NLP) approaches. These models use respiration summary notes, symptoms, and vital signs. To train and test these models, data records containing physiologic signals and vital signs time series were used. These records were captured from patient monitors and comprehensive clinical data obtained from hospital medical information systems for tens of thousands of Intensive Care Unit (ICU) patients. We achieved an area under the Receiver operating characteristic (ROC) curve of 0.82 in detection and prediction of COPD exacerbation.

Related papers

• A multi-cohort study on prediction of acute brain dysfunction states using selective state space models [12.0129301272171]
  acute brain dysfunction (ABD) is a critical challenge due to its prevalence and severe implications for patient outcomes. Our research attempts to solve these problems by harnessing Electronic Health Records (EHR) data. Existing models solely predict a single state (e.g., either delirium or coma) require at least 24 hours of observation data to make predictions. Our research fills these gaps in the existing literature by dynamically predicting delirium, coma, mortality and fluctuating for 12-hour intervals throughout an ICU stay.
  arXiv  Detail & Related papers  (2024-03-11T22:58:11Z)
• 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)
• Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients [6.1594622252295474]
  We develop a prognostic model that predicts if a patient will experience deterioration in the forthcoming 3-24 hours. The model processes routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. We train and evaluate the model using data collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA.
  arXiv  Detail & Related papers  (2022-10-12T02:53:43Z)
• 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)
• Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers [0.0]
  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.
  arXiv  Detail & Related papers  (2020-12-10T07:33:59Z)
• Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS [2.257929280955475]
  The number of infected people in the United States is the highest globally (7.9 million) We analyze the long-term daily logs of blood pressure and heart rate associated with 70 ARDS patients admitted to five University of California academic health centers. Using only the first eight days of the data, our deep learning model is able to achieve 78.79% accuracy to classify the vital signs of ARDS patients infected with COVID-19.
  arXiv  Detail & Related papers  (2020-11-12T19:56:27Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan [49.209225484926634]
  We propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification. Our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.
  arXiv  Detail & Related papers  (2020-05-07T12:16:37Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)

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.