By using an artificially intelligent algorithm to predict patient mortality, a research team from Stanford University is hoping to improve the timing of end-of-life care for critically ill patients.
After parsing through 2 million records, the researchers identified 200,000 patients suitable for the project. The researchers were “agnostic” to disease type, disease stage, severity of admission (ICU versus non-ICU), and so on. All of these patients had associated case reports, including a diagnosis, the number of scans ordered, the types of procedures performed, the number of days spent in the hospital, medicines used, and other factors.
The deep learning algorithm studied the case reports from 160,000 of these patients, and was given the directive: “Given a patient and a date, predict the mortality of that patient within 12 months from that date, using EHR data of that patient from the prior year.” The system was trained to predict patient mortality within the next three to 12 months. Patients with less than three months of lifespan weren’t considered, as that would leave insufficient time for palliative care preparations.
Armed with its new skills, the algorithm was tasked with assessing the remaining 40,000 patients. It did quite well, successfully predicting patient mortality within the 3 to 12 month timespan in nine out of 10 cases. Around 95 percent of patients who were assessed with a low probability of dying within that period lived beyond 12 months. The pilot study proved successful, and the researchers are now hoping their system will be applied more broadly.