Machine learning programs use routinely collected hospital data to predict risk of death

Reviewed by Emily Henderson, B.Sc.Mar 21 2023

If you've ever been admitted to hospital or visited an emergency department, you've likely had an electrocardiogram, or ECG, a standard test involving tiny electrodes taped to your chest that checks your heart's rhythm and electrical activity.

Hospital ECGs are usually read by a doctor or nurse at your bedside, but now researchers are using artificial intelligence to glean even more information from those results to improve your care and the health-care system all at once.

In recently published findings, the research team built and trained machine learning programs based on 1.6 million ECGs done on 244,077 patients in northern Alberta between 2007 and 2020. The algorithm predicted the risk of death from that point for each patient from all causes within one month, one year and five years with an 85 per cent accuracy rate, sorting patients into five categories from lowest to highest risk. The predictions were even more accurate when demographic information (age and sex) and six standard laboratory blood test results were included.

The study is a proof-of-concept for using routinely collected data to improve individual care and allow the health-care system to "learn" as it goes, according to principal investigator Padma Kaul, professor of medicine and co-director of the Canadian VIGOUR Centre.

We wanted to know whether we could use new methods like artificial intelligence and machine learning to analyze the data and identify patients who are at higher risk for mortality. These findings illustrate how machine learning models can be employed to convert data collected routinely in clinical practice to knowledge that can be used to augment decision-making at the point of care as part of a learning health-care system."

Padma Kaul, professor of medicine and co-director of the Canadian VIGOUR Centre

A clinician will order an electrocardiogram if you have high blood pressure or symptoms of heart disease, such as chest pain, shortness of breath or an irregular heartbeat. The first phase of the study examined ECG results in all patients, but Kaul and her team hope to refine these models for particular subgroups of patients. They also plan to focus the predictions beyond all-cause mortality to look specifically at heart-related causes of death.

"We want to take data generated by the health-care system, convert it into knowledge and feed it back into the system so that we can improve care and outcomes. That's the definition of a learning health-care system."