Stories about: machine learning

Forecasting the convergence of artificial intelligence and precision medicine

Image of artificial DNA, which in combination with other artificial intelligence could contribute to an artificial model of the immune system
Will an artificial model of the immune system be the key to discovering new, precision vaccines?

This is part I of a two-part blog series recapping the 2018 BIO International Convention.

At the 2018 BIO International Convention last week, it was clear what’s provoking scientific minds in industry and academia — or at least those of the Guinness-world-record-making 16,000 people in attendance. Artificial intelligence, machine learning and their implications for tailor-made medicine bubbled up across all BIO’s educational tracks and a majority of discussions about the future state of biotechnology. Panelists from Boston Children’s Hospital also contributed their insights to what’s brewing at the intersection of these burgeoning fields.

Isaac Kohane, MD, PhD, former chair of Boston Children’s Computational Health and Informatics Program, spoke on a panel about how large-scale patient data — if properly harnessed and analyzed for health and disease trends — is a virtual goldmine for precision medicine insights. Patterns gleaned from population health data or electronic health records, for example, could help identify which subgroups of patients who might respond better to specific therapies.

According to Kohane, who is currently the Marion J. Nelson Professor of Biomedical Informatics and Pediatrics at Harvard Medical School (HMS), we will soon be leveraging artificial intelligence to go through patient records and determine exactly what doctors were thinking when they saw patients.

“We’ve seen again and again that data abstraction by artificial intelligence is better than abstraction by human analysts when performed at the scale of millions of clinical notes across thousands of patients,” said Kohane.

And based on what we heard at BIO, artificial intelligence will revolutionize more than patient data mining. It will also transform the way we design precision therapeutics — and even vaccines — from the ground up.

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Lessons from the data: Applying machine learning for clinical decision support

machine learning clinical decision support

Mauricio Santillana, PhD, faculty member in the Computational Health Informatics Program at Boston Children’s Hospital, had an idea as he witnessed the volume of continuous real-time data generated in the pediatric intensive care unit (PICU). He realized that tapping the data on patients’ ever-changing vital signs, with the help of machine-learning algorithms, could support clinical decision-making and predict (and help head off) up-coming health issues.

He started a dialogue with the hospital’s Innovation & Digital Health Accelerator, and now collaborates closely with clinicians in the PICU to create machine-learning algorithms that can help them provide the highest level of care.

“It’s fairly recent that clinicians realized people with backgrounds in math and statistics can be very helpful in a clinical context,” says Santillana

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