I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First. ...Full Bio
I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First.
Success story of Haptik
500 days ago
Who is afraid of automation?
500 days ago
What's happening in AI, Blockchain & IoT
501 days ago
3 million at risk from the rise of robots
501 days ago
Artificial Intelligence to predict death for better end-of-life care
It may sound like a an episode from "Black Mirror", when someone talks about predicting when patients may die using artificial intelligence, but Standford University Researchers feel using AI for this purpose will help patients & physicians to have necessary end-of-life conversations earlier.
It is quite true, that many physicians often provide overly optimistic estimates about when their patients would die and this delays having the difficult conversations about end-of-life options. This can lead to patients receiving expensive, unwanted & aggressive treatment options in the hospital at their time of death rather than being allowed to die peacefully in comfort. The alternative being tested by a Stanford University team would use AI to help physicians screen for newly-admitted patients who could benefit from talking about palliative care choices.
Past studies have shown that about 80 percent of Americans would prefer to spend their last days at home if possible. In reality, up to 60 percent of Americans end up dying in an acute care hospital while receiving aggressive medical treatments, according to research cited by the Stanford group's paper "Improving Palliative Care with Deep Learning" published on the arXiv preprint server.
Palliative care experts usually wait for the medical team in charge of a given patient to request 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. But Stephanie Harman, an internal medicine physician and founding medical director of Palliative Care Services for Stanford Health Care, saw an opportunity to flip that routine around by giving palliative care physicians the ability to identify and proactively reach out to patients.
Harman took her idea to Nigam Shah, associate professor of medicine and biomedical informatics at Stanford University. Shah had been talking about possible collaborations involving AI in healthcare with Andrew Ng, an adjunct professor at Stanford University and former head of the Baidu AI Group. They agreed that the palliative care idea seemed like a good project to explore together.
The Stanford team's AI algorithms rely upon deep learning, the popular machine learning technique that uses neural networks to filter and learn from huge amounts 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. (Predicting the death of a patient within three months would provide too little time for the preparations needed in palliative care.)
"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."
The pilot study's use of an algorithm to predict patient mortality - which was approved by an institutional review board - turns out to be less scary than one might think. From an ethics and medical care standpoint, the deep learning model's assistance in helping human physicians screen patients for palliative care generally comes with major benefits and few downsides.
"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.
Fortunately, the reasoning behind the deep learning model's mortality predictions does not particularly matter in this case. The palliative care team is primarily concerned with accurately identifying patients who could benefit from their attention, as opposed to needing to know exactly why the algorithm predicts a given patient might die within a year.
Still, it may be useful to know why the deep learning model made its predictions for research purposes. In this case, the Stanford group used a common error analysis technique, called ablation analysis, to provide some insight behind the deep learning model's decision-making. Their method involved tuning the model little by little through tweaking individual parameters to figure out what impact those parameters had on the model's decisions.
The Stanford group also emphasized that patients do not need to be standing near death's door in order to benefit from palliative care. The early stages of the pilot study showed 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, Jung says.
We have to understand that using deep learning models for predicting death is far from sinister. Mortality is a useful measure that is straightforward - is the person dead or not - compared with the researchers main interest of finding out the best timing for patients to receive a visit from the palliative care team.
The Stanford groups wishes to gauge the success of the pilot study based on outcomes like how physicians on both the first-line team & palliative care team caring for the patients behave differently. They are also hoping to see if the AI prescreening can help improve the rate of patients getting their wishes for end-of-life care documented. Moreover, they aim to reduce the number of people who die in ICU against their own interests.