To reduce and track the spread of COVID-19, researchers and provider organizations have increasingly turned to artificial intelligence and machine learning tools to improve their surveillance efforts.

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From predicting patient outcomes to anticipating future hotspots across the country, big data analytics systems have helped health leaders stay ahead of the pandemic, resulting in more efficient care delivery.

However, healthcare organizations’ level of pandemic preparation is only as good as the data available to them. While the industry is no stranger to data issues, the COVID-19 pandemic has brought a host of unique challenges to the forefront of care delivery.

The novel, global nature of the virus has led to significant gaps in COVID-19 data, with inconsistencies in information leaving officials unsure of the effectiveness of public health interventions.

“It’s now well-known that asymptomatic infections are a common phenomenon in the spread of coronavirus. And it’s very important to understand that phenomenon because, depending on how many asymptomatic infections there are, public health interventions might be different,” Lucy Li, PhD, data scientist at the Chan Zuckerberg Biohub, told HealthITAnalytics.

Researchers at the Chan Zuckerberg Biohub are working to overcome this challenge. Using machine learning and cloud computing technology, Li estimated the number of undetected infections at 12 locations in Asia, Europe, and the US over the course of the pandemic.

The results showed that a wide range of infections were undetected in these locations, with the rate of undetected infections as high as over 90 percent in Shanghai.

Additionally, when the virus was first transmitted to these 12 locations, over 98 percent of infections were undetected during the first few weeks of the outbreak. This suggests that the pandemic was already well underway by the time intense testing began to occur.

These findings have important implications for public health policy and provider organizations, Li noted.

“For disease outbreaks where you can detect every single infection, rapid testing and just a small amount of contact tracing is enough to get the epidemic under control. But for coronavirus, because there are so many asymptomatic infections out there, testing alone won’t help control the pandemic,” she said.

“Because usually when you do testing, you’re testing symptomatic patients. But that’s only a subset of the total number of infections out there. You’re really missing a lot of people who are able to spread the infection, but are not quarantining. Being able to get a sense of what that number might be is helpful for allocating resources.”

Li’s research was supported by the AWS Diagnostic Development Initiative, a global effort to accelerate diagnostic research and innovation during the COVID-19 pandemic and to help mitigate future disease outbreaks.

The initiative allows individuals to take advantage of the cloud and other innovative tools, something that Li said was essential for her research.

“The data I’m using are the viral genomes – the viral DNA. As the viral genomes spread through the population, they accumulate mutations. Generally, these mutations are not good or bad, they’re just changes in the genome. Every time the virus is spread to a new person, it could accumulate new mutations. So, if we know how quickly the virus mutates, we can infer how many missing transmission links there were in between the observed genomes,” she said.

“That’s the data I’m fitting the models to. And because there are many different scenarios that could explain what we see in the viral genomes, I have to leverage machine learning and cloud computing to test all of those hypotheses and to see which one can explain the observed changes in the viral genomes.”

These data analytics tools are well-suited to meeting the challenges brought on by COVID-19, Li pointed out.

“In order to try to quantify the unreported infections, we formulate models of how disease spreads in the population. And then we generate many simulations from these models, and we find out which of those simulations fits the data that we see,” she said.

“That allows us to test different levels of under-reporting and understand which of those can best explain the data that we see. That’s not really possible without a lot of computational resources, and it’s a very time-intensive process. The machine learning tool allows us to explore different explanations of the data that we’re seeing, and we can test many hypotheses. It’s a crucial tool for this type of analysis.”

With machine learning and cloud computing technologies, Li was able to streamline a previously time-consuming task.

“Before cloud computing became more common and these big computational resources became available, some of these analyses could take months to run. I’ve seen papers that were based on months of running a very complex model,” Li said.

“But by having access to more computational resources in the cloud, we can shorten that time from months to days, because we’re able to leverage much more memory and better parallelize our analysis.”

The research could help public health officials monitor the rate of under-reporting in real-time, which could indicate how well current surveillance systems are operating.

“The better the current public health surveillance system is at detecting infections, the fewer underreported cases we would have. But if we see the underreported cases increasing, that would suggest that there needs to be more testing in the population. The results of this research can help the public health department determine how much more testing they would need,” Li said.

“This type of research can also help indicate how close we are to the end of the pandemic. By tracking how many people in the population have been infected by the virus or the number of undetected cases, we could get a sense of how far are we from eliminating this disease.”

With the amount of information generated by the COVID-19 pandemic, analytics tools are critical for uncovering new insights and potential solutions.

“Since the start of the pandemic, we’ve racked our brains to figure out what we can do to help the public health departments in reducing the spread of COVID-19. The number one request that we get from public health departments is information. And sometimes, just presenting the raw data to these departments is sufficient by itself,” Li concluded.

“But quite often, we need to use machine learning and mathematical models to infer these parameters or numbers that we can’t directly see in the data. There has been so much effort from different research groups around the world in developing new models to help us tease out the underlying information that’s not obvious from the data alone.”

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Two projects sponsored by Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) have generated solid use cases for machine learning in clinical care.

Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) collaborated in August 2019 to advance innovation in areas including cancer diagnostics, precision medicine, electronic health records, and medical imaging. 

Through the collaboration, researchers from the University of Pittsburgh Medical Center (UPMC), University of Pittsburgh, and Carnegie Mellon University (CMU) received support from Amazon Search Awards on top of existing support from PHDA to use machine learning to dive into various projects.

One of those projects examined machine learning techniques to help experts study breast cancer risk and understand what drives tumor growth. 

Led by Shandong Wu, an associate professor at the University of Pittsburgh department of radiology, a research team analyzed 452 normal mammograms from 226 patients in order to predict the short-term risk of developing breast cancer. 

Wu and his team, who included experts in computer vision, deep learning, bioinformatics, and breast cancer imaging, used two machine learning models and found that both models consistently outperformed in the area of breast density.

Specifically, the team’s model demonstrated between 33 percent and 35 percent improvement over the existing models, researchers highlighted. 

“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” Wu said in the announcement. 

“Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective.” 

Another project led by Eva Szigethy, clinical researcher at UPMC and Louis-Phillippe Morency, associate professor of computer science at CMU, used machine learning to measure changes in an individual’s behavior to diagnosis depression.

Their machine learning models are trained on tens of thousands of language, acoustic, and visual modalities to identify biomarkers for depression. The biomarkers will be compared to results from traditional clinical assessments to determine the accuracy of the machine learning models with identifying depression.

“New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole,” Morency stated. 

AWS and PHDA noted that the projects on breast cancer and depression are just the start when it comes to research collaboration to improve patient care. 

Teams of researchers, healthcare professionals, and machine learning experts will continue to work to understand the risk of aneurysms, predict how cancer cells progress, and aim to improve the electronic health records system. 

“Amazon is excited and encouraged by the progress these researchers are making and how machine learning is central to their work,” said An Luo, senior technical program manager for academic programs at Amazon AI. 

“We look forward to continuing to share how this unique collaboration between the PHDA and AWS is enabling new discoveries to help patients on a global scale.”

For example, David Vorp, PhD, associate dean for research at UPMA, and his research team employed AWS cloud resources to boost the diagnosis and therapy of abdominal aortic aneurysms.

And a CMU research team led by Russell Schwartz, PhD, and Jian Ma, PhD, used machine learning to develop algorithms and software tools to better understand cell origin and evolution. 

“With the latest advances in machine learning, we are developing an algorithm that will provide clinicians with an objective, predictive tool to guide surgical interventions before symptoms appear, improving patient outcomes,” Vorp said in the August announcement.

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September 11, 2020 – A machine learning model was able to detect different clusters of gut bacteria that could potentially identify individuals with existing cardiovascular disease (CVD), according to a study published in the journal Hypertension.

Recent studies have found a link between gut microbiota, the microorganisms in human digestive tracts, and CVD, the leading cause of mortality worldwide. Gut microbiota is highly variable between individuals, and differences in gut microbial compositions between people with and without CVD have been reported.

“Based on our previous research linking gut microbiota to CVD in animal models, we designed this study to test whether it is possible to screen for CVD in humans using artificial intelligence screening of stool samples,” said Bina Joe, PhD, FAHA, the study director, Distinguished University Professor and Chairwoman of the department of physiology and pharmacology at the University of Toledo in Toledo, Ohio.

“Gut microbiota has a profound effect on cardiovascular function, and this could be a potential new strategy for evaluation of cardiovascular health.”

Researchers used data from the American Gut Project, an open platform for microbiome research based in the US. The team leveraged innovative machine learning methods to analyze microbial composition of nearly 1,000 stool samples. Approximately half of the samples were from people with CVD.

Researchers found that the model was able to identify different clusters of gut bacteria that could potentially help identify individuals with and without CVD. The results show the potential for machine learning and artificial intelligence to improve CVD screening.

“Despite the fact that gut microbiomes are highly variable among individuals, we were surprised by the promising level of accuracy obtained from these preliminary results, which indicate fecal microbiota composition could potentially serve as a convenient diagnostic screening method for CVD,” Joe said.

“It is conceivable that one day, maybe without even assessing detailed cardiovascular function, clinicians could analyze the gut microbiome of patients’ stool samples with an artificial machine learning method to screen patients for heart and vascular diseases.”

Researchers have previously used machine learning to improve cardiovascular disease treatment and detection. A study published in Cardiovascular Research showed that machine learning tools could predict patients’ long-term risk of heart attacks and cardiac deaths better than standard methods used by cardiologists.

In that study, researchers found that subjects’ predicted machine learning scores aligned accurately with the actual distribution of observed events.

“In this prospective trial, machine learning demonstrated high performance in risk assessment for myocardial infarction and cardiac death in asymptomatic subjects,” the researchers stated.  

“By objectively combining clinical data and quantitative CT measures, machine learning provided significantly superior risk prediction compared with the coronary artery calcium score. These promising results suggest that machine learning has a potential for clinical implementation to improve risk assessment.”

In a separate study published in 2018, a team from Stanford University were able to create a personal health management tool that combined EHR data and machine learning algorithms to accurately diagnose the heart condition known as abdominal aortic aneurysm (AAA).

Researchers used the hierarchical estimate from agnostic learning (HEAL) health management tool to analyze patients’ genome sequences and EHRs to identify individuals with the cardiovascular disease.

“For each individual, HEAL accurately predicted his/her AAA risk using personal genome and EHR data. On the other hand, for the same individual with newly adopted lifestyles resulting in physiological changes (e.g., from a high cholesterol to a low cholesterol diet), HEAL can immediately update his/her AAA risk upon corresponding changes conditioned on the person’s genome baseline,” Stanford researchers said.

“This allows us to further investigate the interplay between personal genomes and lifestyles underlying disease predisposition.”

As machine learning and AI become more widely used in healthcare, these tools could make the transition from research to clinical care for improved patient health and outcomes. 

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July 31, 2019 – Improving collaboration between specialists and integrating multiple datasets to leverage big data will be key for advancing research for dry age-related macular degeneration (AMD), according to a new report from a National Institute of Health (NIH) working group.

Over 11 million people in the United States are diagnosed with AMD, an eye disease that ultimately results in blindness. It is the leading cause of blindness among individuals 65 years of age and older.

The disease can manifest in one of two forms: neovascular (wet) or non-neovascular (dry). While the neovascular form progresses more rapidly, there are several known and proven treatments for the disease. There are no preventive measures for dry AMD nor treatment options.

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“The working group thoroughly assessed what is known about dry AMD pathobiology, and the recommendations will be informative for considering future NEI research priorities to align with promising pathways for discovering therapeutic targets,” said Director of National Eye Institute (NEI), Paul Sieving, MD, PhD, in an earlier news release.

The working group recommended a systems biology approach to disease treatment, an integration of genomic, preclinical, medical, pharmacological, and clinical data to inform modeling of the disease progression. Synthesizing big data from all these areas including tissue samples from clinical trials will help inform predictive modeling which can then be used to inform individual patient care.

A personalized approach to disease management may also be helpful, the working group recommended. Such an approach should consider the disease stage, progression, and individual risk factors to provide preventive and treatment strategies specific to the patient, the report said. Collaborating will all points of care will allow a multidisciplinary team to use a patient’s unique clinical, imaging, and genomic data to treat the disease.

“We propose that researchers utilize a systems biology approach, integrating the big data available from clinical registries and various fields of biology known as ‘omics’ to develop better models and ultimately treatments for patients with this blinding disease,” stated report co-author Joan W. Miller, MD.

Due to a lack of preventive strategies and treatment options for dry AMD, the working group noted the need for improved understanding of the disease pathology and promoted clinical trial investigations to do so. Previous research has shown a genetic link to the disease as well as several lifestyle factors including smoking, but there is no work examining the effects these factors have on dry AMD.

A better understanding of how these factors impact the disease will help providers be better informed to watch for risk factors and promote inventive preventive strategies. Such understanding only comes from examining data and promoting the use of big, integrated data sources to help investigators use multiple sources to answer their questions.

Effective disease management will need multiple targets that differ based on the disease stage progression, the report notes. A strategy overhaul needs to take place that focuses on large-scale, collaborative, systems biology in order to effectively treat the disease.

“This approach would integrate basic, genomic, pre-clinical, medical, pharmacological, and clinical data into mathematical models of pathological processes at different stages of dry AMD in order to ask how relevant individual components act together within the living system,” Miller said.

The working group was appointed by the National Advisory Eye Council, a 12-member panel that establishes guidelines for the NEI under the NIH. The group was charged with a multilayered goal: to raise public health awareness about the impact of dry AMD, review the current state of research about the disease for a better understanding of its pathology, propose future research directions, encourage scientists to focus on AMD, and promote collaboration among a network of specialized providers.

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