The AI that can tell you when you'll DIE: Stanford reveals 'startlingly accurate' system to predict the end of life for hospital patients
- Stanford researchers trained a deep neural network on 2 million hospital records
- With this, the algorithm learned to accurately predict patients' mortality
- Researchers say this can be used to help pre-screen patients for end-of-life care
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. Stock image
In the new study published pre-print to arXiv, the Stanford team explains that there is often a huge discrepancy between the way a patient wants to live out the rest of their life, and how it actually happens.
According to the researchers, roughly 80 percent of Americans wish to spend their final days at home – but, as many as 60 percent end up dying in the hospital.
In effort to close the gap, the team at Stanford University trained a deep neural network on Electronic Health Record data from the Stanford Hospital and Lucile Packard Children’s hospital, encompassing roughly 2 million adult and child patients, according to IEEE Spectrum.
‘We could build a predictive model using routinely collected operational data in the healthcare setting, as opposed to a carefully designed experimental study,’ Anand Avati, a PhD candidate in computer science at the AI Lab of Stanford University, told IEEE.
‘The scale of data available allowed us to build an all-cause mortality prediction model, instead of being disease or demographic specific.’
The tool isn’t designed to work by itself to guide the care process.
Instead, it could be used in conjunction with assessments by the human doctor to make proactive decisions in pre-screening patients for end-of-life planning.
As the researchers explain, it isn’t always easy to understand who needs palliative care and when.
‘The criteria for deciding which patients benefit from palliative care can be hard to state explicitly,’ the authors explain in the paper.
‘Our approach uses deep learning to screen patients admitted to the hospital to identify those who are most likely to have palliative care needs.
‘The algorithm addresses a proxy problem – to predict the mortality of a given patient within the next 12 months – and use that prediction for making recommendations for palliative care referral.’

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. Stock image
While the process may be helpful, there is one challenge – based on the ‘black box’ nature of the algorithm, the researchers don’t know exactly what its predictions are based on.
In this type of application, however, they say knowing why it made the predictions isn’t necessarily important.
‘The palliative care intervention is not tied to why somebody is getting sick,’ research scientist Kenneth Jung told IEEE.
‘If it was a different hypothetical case of “somebody is going to die and we need to pick treatment options,” in that case we do want to understand the causes because of the treatment.
‘But in this setting, it doesn’t matter as much as long as we get it right.’
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