Penn Medicine researchers have developed a machine learning algorithm that identifies oncology patients at risk of short-term mortality who need end-of-life conversations with clinicians. In a study of 26,525 patients receiving outpatient oncology care, the algorithm accurately predicted patients with cancer who were at risk of six-month mortality using electronic health records, including whether a patient had high blood pressure as well as laboratory and electrocardiogram data. The study found that 51 percent of the patients the algorithm identified as "high priority" for end-of-life conversations died within six months vs. fewer than 4 percent in the "lower priority" group. "Our findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values," concludes the study, which was published in the journal JAMA Network Open. Initially, researchers developed, validated and compared three ML models--gradient boosting, logistic regression and random forest--to estimate six-month mortality among patients seen in oncology clinics affiliated with a large academic cancer center. However, the random forest model in the study demonstrated the best predictive results.

Stanford researchers have developed an AI that can predict when a patient will die with up to 90 percent accuracy. While the idea might sound unnerving, the team behind the work says it could vastly improve end-of-life care for patients and their families. By more accurately pinpointing when a terminal or seriously ill patient may pass, caregivers can prioritize their wishes and ensure important conversations are held before it's too late. Stanford researchers have developed an AI that can predict when a patient will die with up to 90 percent accuracy. While the idea might sound unnerving, the team behind the work says it could vastly improve end-of-life treatments for patients and their families.