Sometimes, simpler is better.

At the risk of, well, oversimplifying a medical study, that’s one way of summing up the findings of a study recently published at JAMA Network that determined that simple machine learning techniques can predict cirrhosis mortality as accurately as more complicated algorithms.

Cirrhosis is a high-risk condition with a progressive clinical course, the researchers noted, and while prognostic risk models exist for cirrhosis, the team said that these existing scores aren’t always the most comprehensive when assessing mortality risk.

“None of these scores account for a wide range of clinical and psychosocial factors that are likely to be associated with mortality in cirrhosis,” the researchers wrote. “Machine learning techniques have been used to help fill these gaps for cirrhosis but have not seen widespread use.”

For the study, the researchers developed three separate machine learning algorithms using data from a group of patients with cirrhosis seen at 130 VA hospitals from October 1, 2011 to September 30, 2015. The algorithms had varying levels of complexity and range of variables that predicted risk of mortality in cirrhosis.

To achieve a balance among accuracy, interpretability, and feasibility, the team then developed a blended model, called the Cirrhosis Mortality Model (CiMM), which used the variables selected from the machine learning algorithms and implemented them in an accessible platform.

The results showed that all three machine learning models achieved good discrimination when predicting one-year mortality, as well as good calibration. Overall, mortality predictions for year two onward showed similar trends, although overall discrimination fell as time elapsed.

The findings show that simple machine learning techniques can perform as well as more complex models, while also outperforming other traditional methods.

“Better understanding of prognosis can frame patients’ preferences, help prioritize goals of care, and inform decision-making across many medical conditions,” the researchers wrote. “Machine learning models have greatly enhanced the accuracy of such predictions, but their black box analytics have limited their usefulness.”

The team noted that although they used cirrhosis as a use case for their simpler approach, the techniques could be applied to other medical conditions as well, adding that the model can also be easily implemented at the patient and population levels.

“The CiMM can be incorporated into electronic health records from EPIC, Cerner Corporation, and others to easily automate and display prediction scores at the individual patient level. The CiMM can also be incorporated into population dashboards as part of quality improvement strategies,” the group said.

“Within these population management systems, CiMM could identify high-risk patients for linkage to care, vigilant surveillance, and proactive care coordination. Matching the interventions with patients’ risk of mortality may allow more tailored approaches rather than one-size-fits-all strategies, enhancing the overall effectiveness of quality improvement initiatives.”

In short, they noted, “We found that machine learning can help select important variables for more transparent risk scores while maintaining high rates of accuracy.”