Is it possible that an Artificial Intelligence can predict death of a patient? Yes, It is possible. Stanford university researchers see the use of AI as a benign opportunity to help prompt physicians and patients to have necessary end-of-life conversations earlier.
In hospitals, doctors do an estimate for patients life but it is difficult to have conversations about end-of-life options. The human tendency can lead to patients receiving unwanted, expensive and aggressive treatments in a hospital at their time of death instead of being allowed to die more peacefully in relative comfort.
Stanford University team is testing an AI to help physicians screen for newly-admitted patients who could benefit from talking about palliative care choices.
Stanford group’s published a research paper “Improving Palliative Care with Deep Learning” on the arXiv preprint server in which it is stated that up to 60 percent of Americans end up dying in an acute care hospital while receiving aggressive medical treatments.
Stephanie Harman, an internal medicine physician and founding medical director of Palliative Care Services for Stanford Health Care saw that Palliative experts wait for the medical team to request them about their services, which typically include providing relief for patients suffering from serious illnesses and possibly recording end-of-life treatment preferences in a living will.
Harman spoke to Nigam Shah, associate professor of medicine and biomedical informatics at Stanford University about his idea and Shah bring this idea to Andrew Ng, an adjunct professor at Stanford University and former head of the Baidu AI Group and all of them agreed to bring AI into health care for this purpose.
This Artificial Intelligence rely upon deep learning, the machine learning technique that uses neural networks to filter and learn from large number of data. The researchers trained a deep learning algorithm on the Electronic Health Records of about 2 million adult and child patients admitted to either the Stanford Hospital or Lucile Packard Children’s hospital to predict the mortality of a given patient within the next three to 12 months.
”We could build a predictive model using routinely collected operational data in the healthcare setting, as opposed to a carefully designed experimental study,” says Anand Avati, a PhD candidate in computer science at the AI Lab of Stanford University. “The scale of data available allowed us to build an all-cause mortality prediction model, instead of being disease or demographic specific.”
“We think that keeping a doctor in the loop and thinking of this as ‘machine learning plus the doctor’ is the way to go as opposed to blindly doing medical interventions based on algorithms… that puts us on firmer ground both ethically and safety-wise,” says Kenneth Jung, a research scientist at Stanford University.
One potential complication with deep learning algorithms is that even their creators often cannot explain why a deep learning model came up with a particular result. That black box nature of deep learning means it might normally be difficult to tell how the Stanford group’s model comes to the conclusion that any given patient would likely die within a year.
In order to get benefit from palliative care, patient need not to be standing on the door of death but pilot study shows that it was often beneficial for physicians to have the end-of-life discussions with seriously ill patients even if they were not likely to die within the next year.
“We want to make sure the sickest patients and their families get a chance to talk about what they want to happen before they become critically ill and they end up in the ICU,” Jung says.
The research will open the discussion about better end-of-life options and that is the best part of that.
