Author: Alex Ouyang
How Machines Learned to Discover Drugs
When I first became a doctor, I cared for an older man whom I’ll call Ted. He was so sick with pneumonia that he was struggling to breathe. His primary-care physician had prescribed one antibiotic after another, but his symptoms had only worsened; by the time I saw him in the hospital, he had a high fever and was coughing up blood. His lungs seemed to be infected with methicillin-resistant Staphylococcus aureus (MRSA), a bacterium so hardy that few drugs can kill it. I placed an oxygen tube in his nostrils, and one of my colleagues inserted an I.V. into his arm. We decided to give him vancomycin, a last line of defense against otherwise untreatable infections.Ted recovered with astonishing speed. When I stopped by the next morning, he smiled and removed the oxygen tube, letting it dangle near his neck like a pendant. Then he pointed to the I.V. pole near his bed, where a clear liquid was dripping from a bag and into his veins.
“Where did that stuff come from?” Ted asked.
“The pharmacy,” I said.
“No, I mean, where did it come from?”
At the time, I could barely pronounce the names of medications, let alone hold forth on their provenance. “I’ll have to get back to you,” I told Ted. He was discharged before I could. But, in the years that followed, I often thought about his question. Every day, I administer medicines whose origins are a mystery to me. I occasionally meet a patient for whom I have no effective treatment to offer, and Ted’s inquiry starts to seem existential. Where do drugs come from, and how can we get more of them? Learn more
Study: When allocating scarce resources with AI, randomization can improve fairness
Organizations are increasingly utilizing machine-learning models to allocate scarce resources or opportunities. For instance, such models can help companies screen resumes to choose job interview candidates or aid hospitals in ranking kidney transplant patients based on their likelihood of survival.When deploying a model, users typically strive to ensure its predictions are fair by reducing bias. This often involves techniques like adjusting the features a model uses to make decisions or calibrating the scores it generates.
However, researchers from MIT and Northeastern University argue that these fairness methods are not sufficient to address structural injustices and inherent uncertainties. In a new paper, they show how randomizing a model’s decisions in a structured way can improve fairness in certain situations. Learn more
Reverse Engineering Dementia With Human Computer Interaction
With many millions of Americans suffering from Alzheimer’s and dementia, cognitive decline is a major issue. The cost of dementia care is a rude awakening for many families, and patients experiencing the troubling symptoms of these difficulties might despair when they hear that there’s really no “cure,” just treatment. One of the problems is that dementia can look a lot like other forms of cognitive decline, like milder senility. So part of the process is diagnosis. We may not be able to cure dementia. But we can at least get help figuring out how to diagnose it with tools based on something called HCI. What is HCI? It stands for ‘human-computer interaction’. In some ways, it’s pretty much what it sounds like – the study of users and their behaviors in using computers. But it’s also a form of cognitive engineering, and may give us a window into the human mind. Looking at stylus-based interaction tasks, scientists are pondering quite a few metrics that reveal details on what people are thinking: eye fixation, blink rate, pupil size, etc. That in turn can help with the epidemic of cognitive impairment as we age (as presented by Randall Davis in this presentation. Davis also showed us some of the new technology coming down the pike). Learn more
A smarter way to streamline drug discovery
The use of AI to streamline drug discovery is exploding. Researchers are deploying machine-learning models to help them identify molecules, among billions of options, that might have the properties they are seeking to develop new medicines.But there are so many variables to consider — from the price of materials to the risk of something going wrong — that even when scientists use AI, weighing the costs of synthesizing the best candidates is no easy task.
The myriad challenges involved in identifying the best and most cost-efficient molecules to test is one reason new medicines take so long to develop, as well as a key driver of high prescription drug prices.
To help scientists make cost-aware choices, MIT researchers developed an algorithmic framework to automatically identify optimal molecular candidates, which minimizes synthetic cost while maximizing the likelihood candidates have desired properties. The algorithm also identifies the materials and experimental steps needed to synthesize these molecules. Learn more
Say Hello to Your Addiction Risk Score — Courtesy of the Tech Industry
MIT Assistant Professor of EECS and Jameel Clinic Principal Investigator Marzyeh Ghassemi spoke with New York Times Opinion Contributor Maia Szalavitz on how the task of addiction prediction and prevention could potentially perpetuate biases in medical decision making. Learn more
