Long-term Neurological Sequelae in Post-COVID-19 Patients: A Machine Learning Approach to Predict Outcomes

• URL: http://arxiv.org/abs/2309.09993v1
• Date: Fri, 15 Sep 2023 21:36:43 GMT
• Title: Long-term Neurological Sequelae in Post-COVID-19 Patients: A Machine Learning Approach to Predict Outcomes
• Authors: Hayder A. Albaqer, Kadhum J. Al-Jibouri, John Martin, Fadhil G. Al-Amran, Salman Rawaf, Maitham G. Yousif
• Abstract summary: The primary aim was to predict outcomes using a machine learning approach based on diverse clinical data and neuroimaging parameters. The application of machine learning models showed promising results in predicting long-term neurological outcomes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The COVID-19 pandemic has brought to light a concerning aspect of long-term neurological complications in post-recovery patients. This study delved into the investigation of such neurological sequelae in a cohort of 500 post-COVID-19 patients, encompassing individuals with varying illness severity. The primary aim was to predict outcomes using a machine learning approach based on diverse clinical data and neuroimaging parameters. The results revealed that 68% of the post-COVID-19 patients reported experiencing neurological symptoms, with fatigue, headache, and anosmia being the most common manifestations. Moreover, 22% of the patients exhibited more severe neurological complications, including encephalopathy and stroke. The application of machine learning models showed promising results in predicting long-term neurological outcomes. Notably, the Random Forest model achieved an accuracy of 85%, sensitivity of 80%, and specificity of 90% in identifying patients at risk of developing neurological sequelae. These findings underscore the importance of continuous monitoring and follow-up care for post-COVID-19 patients, particularly in relation to potential neurological complications. The integration of machine learning-based outcome prediction offers a valuable tool for early intervention and personalized treatment strategies, aiming to improve patient care and clinical decision-making. In conclusion, this study sheds light on the prevalence of long-term neurological complications in post-COVID-19 patients and demonstrates the potential of machine learning in predicting outcomes, thereby contributing to enhanced patient management and better health outcomes. Further research and larger studies are warranted to validate and refine these predictive models and to gain deeper insights into the underlying mechanisms of post-COVID-19 neurological sequelae.

Related papers

• Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs [6.5527554277858275]
  We present the first causal temporal framework to model the continuous temporal evolution of disease progression via Neural Differential Equations (NSDE) Our results present the first successful uncertainty-based causal Deep Learning (DL) model to accurately predict future patient MS disability (e.g. EDSS) and treatment effects.
  arXiv  Detail & Related papers  (2024-06-18T17:22:55Z)
• Survival Prediction Across Diverse Cancer Types Using Neural Networks [40.392772795903795]
  Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies. Medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes. This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
  arXiv  Detail & Related papers  (2024-04-11T21:47:13Z)
• 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)
• Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models [0.0]
  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.
  arXiv  Detail & Related papers  (2023-09-27T22:52:08Z)
• 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)
• Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy [1.759008116536278]
  Seven dif-ferent categorization algorithms were used to analyze the data. The support vector machine (SVM) with the linear kernel yielded 76.1% in terms of accuracy.
  arXiv  Detail & Related papers  (2023-02-10T13:04:47Z)
• 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)
• Patient-independent Epileptic Seizure Prediction using Deep Learning Models [39.19336481493405]
  The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects.
  arXiv  Detail & Related papers  (2020-11-18T23:13:48Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• 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)
• From predictions to prescriptions: A data-driven response to COVID-19 [42.57407485467993]
  We propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19. We build personalized calculators to predict the risk of infection and mortality. We propose an optimization model to re-allocate ventilators and alleviate shortages.
  arXiv  Detail & Related papers  (2020-06-30T03:34:00Z)

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.