and lab test results as time passed—serving as indicators of illness severity and type of infection—and an innovative method devised to compare outcomes for patients who did and did not receive antibiotics at a specific time.
"That way, we can predict the counterfactual outcome, and train the counterfactual treatment model to find whether treatment for sepsis works or not." Antibiotics don't come without risks—they can be toxic to kidneys, prompt an allergic reaction or lead to C. difficile, an infection that causes severe diarrhea and inflammation of the colon.
Those insights—and availability of electronic health record data—were important to feeding the model with the right kind of data and designing it to take into account multiple considerations that come with changing medical circumstances, Zhang said., we always train the model batch by batch—you need the model to analyze the pattern of data, set parameters, and based on these parameters, add another training dataset to make improvements.