Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models

• URL: http://arxiv.org/abs/2309.16059v1
• Date: Wed, 27 Sep 2023 22:52:08 GMT
• Title: Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models
• Authors: Maitham G. Yousif, Hector J. Castro
• Abstract summary: The COVID-19 pandemic has globally posed numerous health challenges, notably the emergence of post-COVID-19 cardiovascular complications. This study addresses this by utilizing data-driven machine learning models to predict such complications in 352 post-COVID-19 patients from Iraq.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The COVID-19 pandemic has globally posed numerous health challenges, notably the emergence of post-COVID-19 cardiovascular complications. This study addresses this by utilizing data-driven machine learning models to predict such complications in 352 post-COVID-19 patients from Iraq. Clinical data, including demographics, comorbidities, lab results, and imaging, were collected and used to construct predictive models. These models, leveraging various machine learning algorithms, demonstrated commendable performance in identifying patients at risk. Early detection through these models promises timely interventions and improved outcomes. In conclusion, this research underscores the potential of data-driven machine learning for predicting post-COVID-19 cardiovascular complications, emphasizing the need for continued validation and research in diverse clinical settings.

Related papers

• Predicting Long-term Renal Impairment in Post-COVID-19 Patients with Machine Learning Algorithms [0.0]
  The COVID-19 pandemic has had far-reaching implications for global public health. renal impairment has garnered particular attention due to its potential long-term health impacts. This study endeavors to predict the risk of long-term renal impairment using advanced machine learning algorithms.
  arXiv  Detail & Related papers  (2023-09-28T14:44:06Z)
• Cognizance of Post-COVID-19 Multi-Organ Dysfunction through Machine Learning Analysis [0.0]
  This research paper focuses on the application of machine learning techniques to analyse and predict multi-organ dysfunction. Post-COVID-19 Syndrome presents a wide array of persistent symptoms affecting various organ systems, posing a significant challenge to healthcare.
  arXiv  Detail & Related papers  (2023-09-27T22:25:49Z)
• Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers [69.81176740997175]
  We evaluate the performance of transformer-based models in predicting postoperative acute renal failure, pulmonary complications, and postoperative in-hospital mortality. Our results demonstrate that transformer-based models can achieve superior performance in predicting postoperative complications and outperform traditional machine learning models.
  arXiv  Detail & Related papers  (2023-06-01T14:08:05Z)
• COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data [66.43957431843324]
  We introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization.
  arXiv  Detail & Related papers  (2022-04-24T07:38:37Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study [1.5308395762165423]
  We develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization.
  arXiv  Detail & Related papers  (2021-09-01T04:46:27Z)
• COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics [116.6248556979572]
  COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data. It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
  arXiv  Detail & Related papers  (2021-03-18T03:31:33Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• Building Deep Learning Models to Predict Mortality in ICU Patients [0.0]
  We propose several deep learning models using the same features as the SAPS II score. Several experiments have been conducted based on the well known clinical dataset Medical Information Mart for Intensive Care III.
  arXiv  Detail & Related papers  (2020-12-11T16:27:04Z)
• Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review [5.237999056930947]
  The emergence of COVID-19 has necessitated many efforts by the scientific community for its proper management. The use of Deep Learning (DL) and Artificial Intelligence (AI) can be sought in all of the above-mentioned spheres. It can be evaluated that DL and AI can be effectively implemented to withstand the challenges posed by the global emergency.
  arXiv  Detail & Related papers  (2020-11-25T07:02:14Z)
• Classification supporting COVID-19 diagnostics based on patient survey data [82.41449972618423]
  logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19 were generated. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. This data set consists of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
  arXiv  Detail & Related papers  (2020-11-24T17:44:01Z)

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.