AI predicts circulatory failure in ICU

Researchers at ETH Zurich and Bern University Hospital have developed a method for predicting circulatory failure in patients in intensive care units – enabling clinicians to intervene at an early stage. Their approach uses machine learning methods to evaluate an extensive body of patient data.

Photo
Researchers have developed a method for predicting circulatory failure in patients in ICUs.
Source: Kiryl Lis / Adobe Stock

Patients in a hospital’s intensive care unit are kept under close observation: clinicians continuously monitor their vital signs such as their pulse, blood pressure and blood oxygen saturation. This furnishes doctors and nurses with a wealth of data about the condition of their patients’ health. Nevertheless, using this information to predict how their condition will develop or to detect life-threatening changes far in advance is anything but easy.

Researchers at ETH Zurich and the Bern University Hospital have now developed a method that cleverly combines a patient’s various vital signs with other medically relevant information. Fusing this data enables critical circulatory failure to be predicted several hours before it occurs. In future, the aim is to use the method for real-time evaluation of hospital patients’ vital signs to provide an early warning system for the medical staff on duty, who, in turn, can take appropriate action at an early stage.     

Extensive dataset

The researchers were able to develop this approach thanks to the wealth of data supplied by the Department of Intensive Care Medicine at Bern University Hospital. In 2005, it became the first large intensive care unit in Switzerland to start storing granular, high-resolution data for intensive care patients in digital form. For their study, the researchers used anonymised data from 36,000 admissions to intensive care units, which came exclusively from patients who agreed to their data being used for research purposes.

On the initiative of Tobias Merz, research associate and former senior physician at the Department of Intensive Care Medicine at the University Hospital in Bern and who now works at Auckland City Hospital, researchers led by ETH professors Gunnar Rätsch and Karsten Borgwardt analysed this data using machine learning methods. “The algorithms and models we developed were able to predict 90 percent of all circulatory failures in the dataset we used. In 82 percent of the cases, the prediction came at least two hours in advance, which would have given doctors at least two hours to intervene,” explains Rätsch, Professor of Biomedical Informatics at ETH Zurich.

Relatively few variables required

For each patient in their study, the researchers had several hundred different variables combined with other medical information at their disposal. “However, we were able to show that just 20 of these variables are sufficient to make accurate predictions. These include blood pressure, pulse, various blood values, the patient’s age and the medication administered,” explains Borgwardt, Professor of Data Mining at ETH Zurich.

To further improve the quality of the predictions, the researchers plan to incorporate patient data from other large hospitals into future analyses. In addition, they will make the anonymised dataset, the algorithms and the models available to other scientists. “Preventing circulatory failure is a crucial aspect of patient treatment in intensive care. Even short periods of inadequate circulation significantly increase the mortality of patients,” Merz says. “In intensive care units today, we have to deal with a multitude of alarm systems, but they’re not very accurate. Often, they trigger false alarms or they give us only a short advance warning, which can delay initiation of adequate measures to support a patients circulation,” he says. With their approach, the researchers aim to replace the large number of alarms with a few, highly relevant and early alarms. This is possible, as the study showed that the new method could cut the number of alarms by 90 percent.

Some further development work is required to make the method ready for use as an early warning system. Rätsch explains that the first prototype already exists, but before the system can be employed in everyday clinical practice, its reliability must be demonstrated in clinical studies.

Subscribe to our newsletter

Related articles

Neural networks helps grow artificial organs

Neural networks helps grow artificial organs

Researchers have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish.

Ultrasonic sensors for postoperative bladder monitoring

Ultrasonic sensors for postoperative bladder monitoring

The Fraunhofer IBMT is developing the miniaturized ultrasound system for automated monitoring of bladder irrigation.

AI algorithm to help manage diabetes

AI algorithm to help manage diabetes

Researchers, using artificial intelligence and automated monitoring, have designed a method to help people with type 1 diabetes better manage their glucose levels.

Machine learning enhances brain tumour diagnosis

Machine learning enhances brain tumour diagnosis

A new AI approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report.

Using machine learning to estimate COVID-19’s seasonal cycle

Using machine learning to estimate COVID-19’s seasonal cycle

Scientists are launching a project to apply machine learning methods to assess the role of climate variables in disease transmission

Wearable tracks COVID-19 key symptoms

Wearable tracks COVID-19 key symptoms

Researchers have developed a wearable device to catch early signs and symptoms associated with COVID-19 and to monitor patients as the illness progresses.

Machine learning system crack COVID-19 genome signature

Machine learning system crack COVID-19 genome signature

Using machine learning, a team of Western computer scientists and biologists have identified an underlying genomic signature for 29 different COVID-19 DNA sequences.

Fighting hand tremors with AI and robots

Fighting hand tremors with AI and robots

Researchers have tapped AI techniques to build an algorithmic model that will make the robots more accurate, faster, and safer when battling hand tremors.

AI challenge aims to improve mammography accuracy

AI challenge aims to improve mammography accuracy

AI techniques, used in combination with the evaluation of expert radiologists, improve the accuracy in detecting cancer using mammograms.

Popular articles