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Rutgers Researchers Develop Groundbreaking Machine Learning Model to Predict COVID-19 Severity

machine learning model

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TL;DR

  • The six-factor model effectively predicts the likelihood of death in cases of COVID-19, offering a valuable tool for prognosis.
  • Critical variables encompass platelet count, lactate, age, blood urea nitrogen, aspartate aminotransferase, and C-reactive protein, providing a comprehensive framework for assessment.
  • The implementation of this efficient system allows for tailored care and thorough analysis of incoming patients, enhancing the precision of medical interventions.

In a significant leap forward in the battle against the COVID-19 pandemic, researchers at Rutgers have unveiled a pioneering machine learning model capable of predicting the severity of coronavirus cases. The six-factor model, developed by a team of dedicated scientists, offers a groundbreaking approach to assess the prognosis of patients, allowing for more personalized and efficient healthcare.

Predicting COVID-19 severity with the Six-Factor machine learning model

In a recent study led by David Natanov, a student at Robert Wood Johnson Medical School, and the project’s lead author, a new machine learning model has emerged as a beacon of hope in the fight against COVID-19. Recognizing the unique challenges posed by the virus, Natanov and his team sought to address the abnormality of COVID-19’s effects compared to other respiratory illnesses.

The six critical variables, collectively known as PLABAC (platelet count, lactate, age, blood urea nitrogen, aspartate aminotransferase, and C-reactive protein), were identified as key indicators of the likelihood of death in COVID-19 cases. Unlike previous studies that often relied on technologies not readily available in hospital labs, the Rutgers researchers collected data from over 900 cases at Robert Wood Johnson University Hospital during the peak of the pandemic.

Natanov emphasized the necessity of an efficient, accessible COVID-19-specific model, given the limitations of earlier models such as CURB-65 used for pneumonia. After rigorous testing of multiple models, including a 77-variable one, PLABAC emerged as the most predictive set of variables.

For hospitals, the implementation of this model means a revolutionary shift in patient care. Natanov highlighted its ability to objectively analyze incoming patients upon admission, enabling physicians to tailor care based on a patient’s specific case. This becomes particularly crucial in the case of new variants, where the unpredictability of COVID-19 remains a constant threat.

Byron Avihai, a doctoral candidate and researcher on Natanov’s team, stressed the significance of the project for large-scale data collection and patient chart analysis. The developed data collection method, taking a year and a half to refine, holds promise for application to various diseases in the future. Avihai envisioned potential expansions of the study to observe long COVID and its determining factors.

The importance of continuous monitoring

As the research team at Rutgers celebrates this milestone, they acknowledge the ongoing challenges posed by the ever-evolving nature of the virus. Avihai pointed out the constant mutations of the virus, underscoring the importance of close monitoring by healthcare providers. Notably, the discussion turned to the unique characteristics of the Omicron variant, emphasizing its tendency to target pregnant individuals more than its predecessors.

Natanov expressed hope that the PLABAC model could find its way into hospitals, offering families the ability to assess their loved ones’ conditions more accurately. The team remains committed to studying COVID-19 as a clinical entity, recognizing the potential widespread positive impact of the model in various healthcare settings.

The Rutgers researchers’ six-factor machine learning model represents a significant stride in the ongoing battle against the COVID-19 pandemic. As hospitals continue to grapple with the challenges of evolving variants and potential overlapping outbreaks, the implementation of such predictive models promises a more efficient and tailored approach to patient care. This innovative approach not only aids in navigating the complexities of current pandemic challenges but also stands as a beacon of hope for future infectious disease management, marking a paradigm shift in healthcare strategies.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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Aamir Sheikh

Amir is a media, marketing and content professional working in the digital industry. A veteran in content production Amir is now an enthusiastic cryptocurrency proponent, analyst and writer.

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