According to the researchers, these parameters indirectly give information regarding the condition of the patient, with intracranial pressure alone yielding hundreds of thousands of data points per day.
Since, there are millions of daily collected data points from the ICU for a patient, the researchers develop an artificial intelligence (AI) based algorithm to predict the outcome of individual patients, and give objective data regarding their condition and prognosis over the course of treatment.
"A dynamic prognostic model like this has not been presented before. Although this is a proof-of-concept and it will still take some time before we can implement algorithms like this into daily clinical practice, our study reflects how and into what direction modern intensive care is evolving," said study co-author Rahul Raj from the University of Helsinki.
The study noted that the new AI system can predict the probability of the patient dying within 30-days with accuracy of 80-85 per cent.
The scientists created two algorithms -- the first algorithm, a simpler one based only on objective monitor data, and the second a slightly more complex system that includes data on the level of a patient's consciousness, the study said.
"As expected, the accuracy of the more complex algorithm is slightly better than for the simpler algorithm. Still, the accuracy of both algorithms is surprisingly good, considering that the simpler model is based upon only three main variables and the more complex upon five main variables," said Eetu Pursiainen, study co-author from the University of Helsinki.
The researchers hope to validate the algorithms using national and international external datasets.
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