In this way, extensive medical data can be used to automatically determine a strategy which achieves a particularly high probability of success. The computer becomes an agent that makes its own decisions: if the patient is well, the computer is "rewarded." If the condition deteriorates or death occurs, the computer is "punished." The computer programme has the task of maximising its virtual "reward" by taking actions. There has been little research in this regard in the medical field." Mathematically, this is something quite different. "This is not just about simple categorisation - for example, separating a large number of images into those that show a tumour and those that do not - but about a temporally changing progression, about the development that a certain patient is likely to go through. "In our project, we used a form of machine learning called reinforcement learning," says Clemens Heitzinger. However, the computer can easily take many more parameters than a human into account - and in some cases this can lead to even better decisions. Most of the time, they know very well which parameters they have to take into account in order to provide the best care. Medical staff make their decisions on the basis of well-founded rules.
He is also Co-Director of the cross-faculty "Center for Artificial Intelligence and Machine Learning" (CAIML) at TU Wien. Clemens Heitzinger from the Institute for Analysis and Scientific Computing at TU Wien (Vienna). We wanted to investigate whether these data could be used even better than before," says Prof. The patients are constantly monitored medically. "In an intensive care unit, a lot of different data is collected around the clock. However, it is now important to also discuss the legal aspects of such methods. Analyses show that artificial intelligence already surpasses the quality of human decisions. With the help of extensive data from intensive care units of various hospitals, an artificial intelligence was developed that provides suggestions for the treatment of people who require intensive care due to sepsis.
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