Machine learning could help reduce hospitalizations by nearly 30% during a pandemic, study finds
New research shows machine learning can be more effective than current methods to distribute scarce treatments to patients most vulnerable during a public health crisis New research shows machine learning can be more effective than current methods
University of Colorado Anschutz Medical Campus
A new study sheds light on a promising approach using machine learning to more effectively allocate medical treatments during a pandemic or any time there’s a shortage of therapeutics.
The findings, published today in JAMA Health Forum, found a significant reduction in expected hospitalizations when using machine learning to help distribute medication using the COVID-19 pandemic to test the model. The model proves to reduce hospitalizations relatively by about 27 percent compared to actual and observed care.
“During the pandemic, the healthcare system was at a breaking point and many health care facilities relied on a first-come, first-serve or a patient’s health history to implement who received treatments,” said the paper’s senior author Adit Ginde, MD, professor of emergency medicine at the University of Colorado Anschutz Medical Campus.
“However, these methods often don’t address the complex interactions that can occur in patients when taking medications to determine expected clinical effectiveness and may overlook patients who would benefit the most from treatment. We show that machine learning in these scenarios is a way to use real-time, real-world evidence to inform public health decision making,” Ginde adds.
In the study, the researchers showed that using machine learning that looks at how individual patients benefit differently from treatment can provide doctors, health systems and public health officials with more accurate information in real-time than traditional allocation score models. Mengli Xiao, PhD, assistant professor in Biostatistics and Informatics, developed the mAb allocation system based on machine learning.
“Existing allocation methods primarily target patients who have a high-risk profile for hospitalizations without treatments. They could overlook patients who benefit most from treatments. We developed a mAb allocation point system based on treatment effect heterogeneity estimates from machine learning. Our allocation prioritizes patient characteristics associated with large causal treatment effects, seeking to optimize overall treatment benefits when resources are limited” said Xiao, who is also faculty at the Center of Innovative Design and Analysis (CIDA).
Specifically, the researchers looked at the effectiveness of adding a novel Policy Learning Trees (PLTs)-based method for optimizing the allocation of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint.
The PLT approach was designed to decide which treatments to assign to individuals in a way that maximizes the overall benefits for the population (ensuring those who are at the highest risk of hospitalization are sure to receive treatments, especially when treatment is scarce). This is done by taking into account how different factors affect the effectiveness of the treatment.
The researchers compared the machine learning approach with real-world decisions and a standard point allocation system used during the pandemic. They found the PLTs-based model demonstrated a significant reduction in expected hospitalizations compared to the observed allocation. This improvement also surpassed the performance of the Monoclonal Antibody Screening Score, which observes antibodies for diagnosis.
“Using an innovative approach like machine learning expands beyond crises like the COVID-19 pandemic and shows we can provide personalized public health decisions even when resources are limited in any scenario. To do so, though, it’s important that robust, real-time data platforms, like what we developed for this project, are implemented to provide data-driven decisions,” adds Ginde, a leader in the Colorado Clinical and Translational Sciences Institute at CU Anschutz.
The paper in JAMA Health Forum will be the 15th publication to come out of a project called Monoclonal Antibody (mAB) Colorado, which was funded by a grant from the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS). The project focused on doing the most good for the most people, using real world evidence for data-driven decisions during the COVID-19 pandemic.
The researchers hope this paper will encourage public health entities, policymakers and disaster management agencies to look into methods like machine learning to implement in case of a future public health crisis.
About the University of Colorado Anschutz Medical Campus
The University of Colorado Anschutz Medical Campus is a world-class medical destination at the forefront of transformative science, medicine, education and patient care. The campus encompasses the University of Colorado health professional schools, more than 60 centers and institutes, and two nationally ranked independent hospitals - UCHealth University of Colorado Hospital and Children's Hospital Colorado – which see more than 2 million adult and pediatric patient visits yearly. Innovative, interconnected and highly collaborative, the University of Colorado Anschutz Medical Campus delivers life-changing treatments, patient care and professional training and conducts world-renowned research fueled by $705 million in research grants. For more information, visit www.cuanschutz.edu.
Journal
JAMA Health Forum
Article Title
A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies
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