Machine learning can help accurately predict clinical outcomes in patients with heart problems
Several studies being presented at the American College of Cardiology’s 67th Annual Scientific Session demonstrate how the computer science technique known as machine learning can be used to accurately predict clinical outcomes in patients with known or potential heart problems. Collectively, the findings suggest that machine learning may usher in a new era in digital health care tools capable of enhancing health care delivery by aiding routine processes and helping physicians assess patients’ risk.
While clinical scoring systems and algorithms have long been used in medical practice, there has been a marked uptick in the application of machine learning to improve such tools in recent years. In contrast to traditional algorithms that require all calculations to be pre-programmed, machine learning algorithms deduce the optimal set of calculations by looking for patterns in large collections of patient data.
The new studies presented at ACC.18 demonstrate how machine learning can be used to predict outcomes such as diagnosis, death or hospital readmission; improve upon standard risk assessment tools; elucidate factors that contribute to disease progression; or to advance personalized medicine by predicting a patient’s response to treatment.
In one study, researchers used machine learning to predict which patients would ultimately be diagnosed with a heart attack after coming to a hospital emergency department for chest pain. Chest pain is one of the most common complaints among patients visiting the emergency department, yet only a minority of these patients are ultimately diagnosed with a heart attack. In a pilot test, the algorithm showed a 94 percent accuracy rate for predicting the ultimate diagnosis.
“The long-term goal is to identify those who are at highest risk and to treat those patients first,” said Daniel Lindholm, MD, PhD, postdoctoral research fellow at Uppsala University in Sweden and the study’s lead author. “If this work is further validated, hospitals could potentially use it to quickly pick out which patients are sickest when they arrive at the hospital, and that could lead to a shorter time to treatment for those who need it most.”
Lindholm and his colleague developed and tested their algorithm using data from more than 8,200 emergency department visits in Stockholm between 2011 and 2013. In the first (training) phase, the algorithm used data from 5,800 patient visits to iteratively refine decision trees to identify a heart attack diagnosis based on factors such as blood test results, vital signs and the patient’s medical history–the same data that a physician would have upon first encountering a patient.
