A new AI system developed by researchers from Google and the University of California could prevent deaths caused by incorrect prescriptions.
While quite rare, prescriptions that are incorrect – or react badly to a patient’s existing medications – can result in hospitalisation or even death.
In a blog post today, Alvin Rajkomar MD, Research Scientist and Eyal Oren PhD, Product Manager, Google AI, set out their work on using AI for medical predictions.
The AI is able to predict which conditions a patient is being treated for based on certain parameters. “For example, if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia,” the researchers wrote.
In the future, an AI could step in if a medication that’s being prescribed looks incorrect for a patient with a specific condition in their current situation.
“While no doctor, nurse, or pharmacist wants to make a mistake that harms a patient, research shows that 2% of hospitalized patients experience serious preventable medication-related incidents that can be life-threatening, cause permanent harm, or result in death,” the researchers wrote.
“However, determining which medications are appropriate for any given patient at any given time is complex — doctors and pharmacists train for years before acquiring the skill.”
The AI was trained on an anonymized data set featuring around three million records of medications issued from over 100,000 hospitalizations.
In their paper, the researchers wrote:
“Patient records vary significantly in length and density of data points (e.g., vital sign measurements in an intensive care unit vs outpatient clinic), so we formulated three deep learning neural network model architectures that take advantage of such data in different ways: one based on recurrent neural networks (long short-term memory (LSTM)), one on an attention-based TANN, and one on a neural network with boosted time-based decision stumps.
We trained each architecture (three different ones) on each task (four tasks) and multiple time points (e.g., before admission, at admission, 24 h after admission and at discharge), but the results of each architecture were combined using ensembling.”
You can find the full paper in the science journal Nature here.
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