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U.S. Researchers Machine Learning System Flags Treatment that Might Do More Harm Than Good

Sepsis claims the lives of nearly 270,000 people in the U.S. each year. An unpredictable illness can progress quickly, resulting in a speedy drop in blood strain, tissue injury, several organ failures, and demise. Well-timed intervention by healthcare professionals saves lives, however some therapies for sepsis can even worsen an affected person’s situation, so selecting the very best remedy may be difficult. For instance, within the early hours of extreme sepsis, giving an excessive amount of intravenous fluid can improve the affected person’s threat of demise.

To help clinicians avoid remedies that may potentially contribute to a patient’s death, researchers at MIT have developed a machine learning model that could be used to identify treatments that pose a higher risk than other options. Their model can also warn doctors when a septic patient is approaching a medical dead end — the point when the patient will most likely die no matter what treatment is used — so that they can intervene before it is too late.

When utilised to a dataset of sepsis sufferers in a hospital intensive care unit, the investigator mannequin confirmed that about 12% of the therapies for deceased sufferers have been dangerous. The research additionally exhibits that about 3 p.c of sufferers who didn’t survive have been caught in a medical stalemate 48 hours earlier than demise.

“We see that our model is almost eight hours ahead of a doctor’s recognition of a patient’s deterioration. This is powerful because in these really sensitive situations, every minute counts, and being aware of how the patient is evolving, and the risk of administering certain treatment at any given time, is really important.”

Taylor Killian, a graduate student in the Healthy ML group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

This research project was spurred by a paper that explored the usage of reinforcement studying in conditions the place it is too harmful to analyse voluntary actions, making it tough to generate sufficient information to successfully practice algorithms. Conditions the place it is not potential to gather extra information forward of time are known as “offline” settings.

In reinforcement learning, the algorithm is trained through trial and error and learns to take actions that maximise its accumulation of reward. But in a health care setting, it is nearly impossible to generate enough data for these models to learn the optimal treatment, since it isn’t ethical to experiment with possible treatment strategies.

So, the researchers flipped reinforcement learning on its head. They used the limited data from a hospital ICU to train a reinforcement learning model to identify treatments to avoid, with the goal of keeping a patient from entering a medical dead end. Learning what to avoid is a more statistically efficient approach that requires less data.

The researchers also found that 20% to 40% of patients who did not survive raised at least one yellow flag prior to their death, and many raised that flag at least 48 hours before they died. The results also showed that, when comparing the trends of patients who survived versus patients who died, once a patient raises their first flag, there is a very sharp deviation in the value of administered treatments. The window of time around the first flag is a critical point when making treatment decisions.

Moving forward, the researchers also want to estimate causal relationships between treatment decisions and the evolution of patient health. They plan to continue enhancing the model so it can create uncertainty estimates around treatment values that would help doctors make more informed decisions. Another way to provide further validation of the model would be to apply it to data from other hospitals, which they hope to do in the future.


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