How Can Machine Learning Programs Predict Death Risk?

Artificial intelligence helps physicians fetch more data from an electrocardiogram, or ECG to improve patient’s care and well-being. 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 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.

Artificial Intelligence Predicts Mortality Risk from Routine Tests

“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,” Kaul explains. “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.”

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.

Source-Eurekalert