A deep learning algorithm could help providers predict survival expectancy in patients with lung cancer, which could help guide treatment decisions.

A deep learning tool was able to accurately predict survival expectancy in patients with lung cancer, potentially leading to more informed care decisions by providers, according to a study published in the International Journal of Medical Informatics.

The study showed that in certain conditions, the deep learning model was more than 71 percent accurate in predicting survival expectancy of lung cancer patients, compared to other machine learning models that performed with about 61 percent accuracy.

The tool can analyze a large amount of data that describe the patients and the disease to understand how a combination of factors affect lung cancer survival periods. These factors include information like types of cancer, size of tumors, speed of tumor growth, and demographic data.

“This is a high-performance system that is highly accurate and is aimed at helping doctors make these important decisions about providing care to their patients,” said Youakim Badr, associate professor of data analytics at Penn State Great Valley. “Of course, this tool can’t be used as a substitute for a doctor in making decisions on lung cancer treatments.”

Information on a patient’s survival expectancy could help guide providers and caregivers make improved decisions on using medicines, allocating resources, and determining the intensity of care for patients.

Researchers noted that deep learning techniques are uniquely suited to address lung cancer prognosis because the technology can provide the comprehensive analysis needed in cancer research.

In deep learning, developers apply a sophisticated structure of multiple layers of artificial neurons. The learning aspect of deep learning comes from how the system learns from connections between data and labels, the team noted.

“Deep learning is a machine-learning algorithm that makes associations between the data, itself, and the labels that we use to describe the data examples,” said Badr. “By making these associations, it learns from the data.”

The structure of deep learning also offers several advantages for many data science tasks, especially in cases involving large datasets.

“It improves performance tremendously. In deep learning we can go deeper, which is why they call it that. In traditional machine learning, you have a simple structure of layers of neural networks. In each layer, you have a group of cells,” said Robin G. Qiu, professor of information science and engineering and an affiliate of the Institute for Computational and Data Sciences.

“In deep learning, there are many layers of these cells that can be architected into a sophisticated structure to perform better feature transformation and extraction, which gives you the ability to further improve the accuracy of any model.”

Researchers analyzed data from the Surveillance, Epidemiology, and End Results (SEER) program. The SEER dataset is one of the biggest and most comprehensive databases on the early diagnosis information for cancer patients in the US. The program’s cancer registries cover almost 35 percent of US cancer patients.

“One of the really good things about this data is that it covers a large section of the population and it’s really diverse,” said Shreyesh Doppalapudi, a graduate-student research assistant and first author of the paper.

“Another good thing is that it covers a lot of different features, which you can use for many different purposes. This becomes very valuable, especially when using machine learning approaches.”

When the team compared several deep learning approaches to traditional machine learning models, the deep learning approaches performed much better than traditional machine learning methods.

The deep learning technique enabled researchers to find associations in the SEER dataset, which includes about 800,000 to 900,000 entries – something that would have been incredibly challenging without the assistance of AI.

“If it were only three fields I would say it would be impossible — and we had about 150 fields,” said Doppalapudi. “Understanding all of those different fields and then reading and learning from that information, would be impossible.”

Going forward, researchers will aim to improve the model and test its ability to analyze other types of cancers and medical conditions.

“The accuracy rate is good, but it’s not perfect, so part of our future work is to improve the model,” said Qiu.

Researchers also plan to connect with domain experts on specific cancers and medical conditions.

“In a lot of cases, we might not know a lot of features that should go into the model,” said Qiu. “But, by collaborating with domain experts, they could help us collect important features about patients that we might not be aware of and that would further improve the model.”

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Researchers have developed a system to identify drugs that might be repurposed to fight the coronavirus in elderly patients

When the COVID-19 pandemic struck in early 2020, doctors and researchers rushed to find effective treatments. There was little time to spare. “Making new drugs takes forever,” says Caroline Uhler, a computational biologist in MIT’s Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society, and an associate member of the Broad Institute of MIT and Harvard. “Really, the only expedient option is to repurpose existing drugs.”

Uhler’s team has now developed a machine learning-based approach to identify drugs already on the market that could potentially be repurposed to fight COVID-19, particularly in the elderly. The system accounts for changes in gene expression in lung cells caused by both the disease and aging. That combination could allow medical experts to more quickly seek drugs for clinical testing in elderly patients, who tend to experience more severe symptoms. The researchers pinpointed the protein RIPK1 as a promising target for COVID-19 drugs, and they identified three approved drugs that act on the expression of RIPK1.

The research appears today in the journal Nature Communications. Co-authors include MIT PhD students Anastasiya Belyaeva, Adityanarayanan Radhakrishnan, Chandler Squires, and Karren Dai Yang, as well as PhD student Louis Cammarata of Harvard University and long-term collaborator G.V. Shivashankar of ETH Zurich in Switzerland.

Early in the pandemic, it grew clear that COVID-19 harmed older patients more than younger ones, on average. Uhler’s team wondered why. “The prevalent hypothesis is the aging immune system,” she says. But Uhler and Shivashankar suggested an additional factor: “One of the main changes in the lung that happens through aging is that it becomes stiffer.”

The stiffening lung tissue shows different patterns of gene expression than in younger people, even in response to the same signal. “Earlier work by the Shivashankar lab showed that if you stimulate cells on a stiffer substrate with a cytokine, similar to what the virus does, they actually turn on different genes,” says Uhler. “So, that motivated this hypothesis. We need to look at aging together with SARS-CoV-2 — what are the genes at the intersection of these two pathways?” To select approved drugs that might act on these pathways, the team turned to big data and artificial intelligence.

The researchers zeroed in on the most promising drug repurposing candidates in three broad steps. First, they generated a large list of possible drugs using a machine-learning technique called an autoencoder. Next, they mapped the network of genes and proteins involved in both aging and SARS-CoV-2 infection. Finally, they used statistical algorithms to understand causality in that network, allowing them to pinpoint “upstream” genes that caused cascading effects throughout the network. In principle, drugs targeting those upstream genes and proteins should be promising candidates for clinical trials.

To generate an initial list of potential drugs, the team’s autoencoder relied on two key datasets of gene expression patterns. One dataset showed how expression in various cell types responded to a range of drugs already on the market, and the other showed how expression responded to infection with SARS-CoV-2. The autoencoder scoured the datasets to highlight drugs whose impacts on gene expression appeared to counteract the effects of SARS-CoV-2. “This application of autoencoders was challenging and required foundational insights into the working of these neural networks, which we developed in a paper recently published in PNAS,” notes Radhakrishnan.

Next, the researchers narrowed the list of potential drugs by homing in on key genetic pathways. They mapped the interactions of proteins involved in the aging and SARS-CoV-2 infection pathways. Then they identified areas of overlap among the two maps. That effort pinpointed the precise gene expression network that a drug would need to target to combat COVID-19 in elderly patients.

“At this point, we had an undirected network,” says Belyaeva, meaning the researchers had yet to identify which genes and proteins were “upstream” (i.e. they have cascading effects on the expression of other genes) and which were “downstream” (i.e. their expression is altered by prior changes in the network). An ideal drug candidate would target the genes at the upstream end of the network to minimize the impacts of infection.

“We want to identify a drug that has an effect on all of these differentially expressed genes downstream,” says Belyaeva. So the team used algorithms that infer causality in interacting systems to turn their undirected network into a causal network. The final causal network identified RIPK1 as a target gene/protein for potential COVID-19 drugs, since it has numerous downstream effects. The researchers identified a list of the approved drugs that act on RIPK1 and may have potential to treat COVID-19. Previously these drugs have been approved for the use in cancer. Other drugs that were also identified, including ribavirin and quinapril, are already in clinical trials for COVID-19.

Uhler plans to share the team’s findings with pharmaceutical companies. She emphasizes that before any of the drugs they identified can be approved for repurposed use in elderly COVID-19 patients, clinical testing is needed to determine efficacy. While this particular study focused on COVID-19, the researchers say their framework is extendable. “I’m really excited that this platform can be more generally applied to other infections or diseases,” says Belyaeva. Radhakrishnan emphasizes the importance of gathering information on how various diseases impact gene expression. “The more data we have in this space, the better this could work,” he says.

This research was supported, in part, by the Office of Naval Research, the National Science Foundation, the Simons Foundation, IBM, and the MIT Jameel Clinic for Machine Learning and Health.

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In late January, researchers at BenevolentAI, an artificial intelligence startup in central London, turned their attention to the coronavirus.

Within two days, using technologies that can scour scientific literature related to the virus, they pinpointed a possible treatment with speed that surprised both the company that makes the drug and many doctors who had spent years exploring its effect on other viruses.

Called baricitinib, the drug was designed to treat rheumatoid arthritis. Although many questions hang over its potential use as a coronavirus treatment, it will soon be tested in an accelerated clinical trial with the National Institutes of Health. It is also being studied in Canada, Italy and other countries.

The specialists at BenevolentAI are among many AI researchers and data scientists around the world who have turned their attention to the coronavirus, hoping they can accelerate efforts to understand how it is spreading, treat people who have it and find a vaccine.

Before the pandemic, the AI researchers were part of one of the most hyped and well-funded sectors of the tech industry, pursuing visions of autonomous vehicles and machines that can learn by themselves. Now they are simply trying to be helpful — working on technology that augments human experts instead of replacing them.

Medical researchers had spent years exploring baricitinib and similar medications as a way to treat viruses. Baricitinib, a pill taken once a day, can help fight extreme and unwanted activity from the body’s immune system, which occurs with both rheumatoid arthritis and viruses like HIV and can damage healthy cells and tissues.

In late January, after talking with one of the company’s investors in Asia about the pandemic, Baroness Joanna Shields, the chief executive of BenevolentAI, asked Peter Richardson, BenevolentAI’s vice president of pharmacology, if the company could explore potential treatments.

BenevolentAI quickly joined a race to identify drugs that can block the virus from entering the body’s cells. Researchers at the University of California, San Francisco, and many others labs are looking into similar treatments.

BenevolentAI, which has received more than $292 million from the Singapore sovereign wealth fund Temasek, Goldman Sachs and others, had spent the past several years building technology that could help find information buried in vast troves of academic papers and other scientific literature.

The technology was designed for the development of new drugs — not for identifying new uses for existing medications — and it had never been used with material related to viruses.

Using its automated language tools, the company’s engineers generated a detailed and intricately interconnected database of particular biological processes related to the coronavirus. Then Richardson, who is 65 and a trained pharmacologist, used additional tools to browse through what the technology had found and understand what it meant.

“It is not like we have this giant button, and we just smack it, and stuff comes out the other end,” said Olly Oechsle, 37, the software engineer who oversees the design of these tools. “Peter has been working in this area since before I was born.”

“It is not like we have this giant button, and we just smack it, and stuff comes out the other end,” said Olly Oechsle, 37, the software engineer who oversees the design of these tools. “Peter has been working in this area since before I was born.”

Drawing on what the technology found in the literature, Richardson could map out the connections between particular human genes and the biological processes affected by the coronavirus. As a multicolored map appeared on his computer screen, two genes leapt out at him.

“They stood up and said, ‘Look, we’re here,’” Richardson said.

Once the genes were identified, he and his colleagues could pinpoint the way that existing medications targeted the genes, visualizing the process through a kind of digital flow chart. They identified baricitinib, made by the American pharmaceutical giant Eli Lilly.

Many scientists were already considering similar anti-inflammatory drugs that could reduce a cytokine storm, an extreme response from the body’s immune system that can kill coronavirus patients.

But the BenevolentAI researchers went further. Through their software, they found that baricitinib might also prevent the viral infection itself, blocking the way it enters cells. The company said it had no expectations for making money from the research and had no prior relationship with Eli Lilly.

Through Justin Stebbing, a professor of oncology at Imperial College London, the researchers sent their findings to The Lancet, one of Britain’s oldest and most respected medical journals, in early February. Like many other companies and researchers now exploring treatments across the globe, the team wanted to share what it had learned as widely as possible.

The next day, at Emory University Hospital in Atlanta, Dr. Vincent Marconi opened an email from a colleague, Dr. Raymond Schinazi, that pointed him and other colleagues to the paper. They had spent eight years exploring baricitinib and other drugs as a treatment for HIV, and they knew such drugs could potentially help coronavirus patients.

But they had not settled on baricitinib as a viable option, and they had not identified the specific properties that might allow the drug to fight the virus. Nor had the scientists at Eli Lilly.

At Emory, the lab researchers were shocked that the paper had come from BenevolentAI. “It was crazy,” said Christina Gavegnano, who took part in the work with HIV. “We kept asking, ‘Who are these people? Does anyone know them?’”

A month later, Marconi proposed a clinical trial with baricitinib and another drug. As coronavirus cases mounted at his hospital, he and his clinicians administered the pill as a compassionate measure to patients, with encouraging results.

“We normally talk about ‘bench to bedside,’” Stebbing said, referring to moving quickly from laboratory bench research to the treatment of patients. “This is about ‘computer to bench to bedside.’”

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To understand the benefits that artificial intelligence can bring to the world of human medicine, consider the case of Ayako Yamashita, a 60-year-old Japanese woman, whose condition befuddled doctors in 2015.

Yamashita was thought to be suffering from acute myeloid leukemia. But after several unsuccessful treatment attempts, her doctors decided to search for another answer to her condition. They turned to IBM’s Watson, an AI system capable of analyzing vast amounts of data.

The computer reviewed nearly 20 million previously-published oncological research studies and cross-referenced data points. Watson’s analysis suggested the woman had a rare form of leukemia not detected through conventional methods. This led to a change in treatment and doctors crediting Watson for saving  the woman’s life.

The analysis of such huge amount of data is next to impossible for a human mind, but it’s like a walk in a digital park for AI. And it shows what may be one of the valuable things that AI can do for us. It is “the most practical application in the field of medical and healthcare for artificial intelligence,” said Seiji Yamada, of the National Institute of Informatics and chairman of the Japanese Society for Artificial Intelligence.

The global artificial intelligence market is expected to reach $19.47 billion by 2022, according to the research firm Allied Market Research. As AI is marking its presence, tech giants are working to capitalize on new opportunities. The healthcare sector is a natural fit, according to Sanjay Gupta, managing director, South Asia and Middle East for NICE, a technology firm based in Israel, in an interview with ETHealthworld:

“The development of automation enabled by technologies including robotics and artificial intelligence in healthcare sector brings the promise of higher productivity with increased safety.”

Saving lives and time

Among Google’s many AI ventures is an effort to develop new products targeting the health sector. The company is focusing on applications for life preservation, preventive care and improving health care services.

The company plans to launch a trial in India to test an AI system that scans a person’s eyes to look for signs of diabetic retinopathy. The company aims to license the technology to clinics. The system already has proven itself adept at detecting high blood pressure, or risk of heart disease or stroke, according to a study published in early 2018.

From a story published in the Washington Post:

“This may be a rapid way for people to screen for risk,” Harlan Krumholz, a cardiologist at Yale University who was not involved in the study, wrote in an email. “Diagnosis is about to get turbo-charged by technology. And one avenue is to empower people with rapid ways to get useful information about their health.”

Jeff Dean, the Chief at Google AI, outlined for Boss Magazine how this system will enable doctors to better diagnose and treat patients for a range of diseases. Moreover, this system will also track key events in the patient’s past (including hospital stays) to help doctors more effectively.

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