Machine Learning to Predict Injuries

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Ground-breaking research recently used big data analysis and machine learning to develop a model in order to predict the development of pressure injuries among critical care patients. Such models, once successful, have vast potential and could be tailored to serve in military medical facilities in order to predict similar pressure injuries. Perhaps in the future it could be further utilized to predict and coincidently better treat other types of injuries, whether in soldiers or civilians.

The research was published in the November issue of American Journal of Critical Care (AJCC). The research team examined five years of data on patients admitted to the adult surgical or surgical cardiovascular intensive care units at the University of Utah Hospital in Salt Lake City.

Among the sample of 6,376 patients, hospital-acquired pressure injuries of stage 1 or greater developed in 516 patients, and injuries of stage 2 or greater developed in 257 patients.

With these two outcome variables identified by the team, the researchers proceeded to use machine learning to analyse the large amount of clinical data readily available in the patient records, and examine the relationships among the available predictor variables.

They used a technique called random forest, which is relatively unaffected by moderate correlations among variables common in health research.

The researchers believe their study is the only one in which machine learning was used to predict development of pressure injuries in critical care patients.

According to Principal investigator Jenny Alderden, “current risk-assessment tools classify most critical are patients as high risk for developing pressure injuries and therefore do not provide a way to differentiate among critical care patients in terms of pressure injury risk.”

She adds that “eventually, our model may offer additional insight to clinicians as they develop a plan of care for patients at highest risk and identify those who would benefit most from interventions that are not financially feasible for every patient.”

Eventually, the model could help identify which patients are at the greatest risk for developing pressure injuries and who would benefit from interventions such as specialty beds or more frequent skin inspection. The next step will be to validate and evaluate the model in a new sample of patients, as published in newswise.com.