Google AI can predict when you will die

Google can predict when you’ll die using artificial intelligence

The research tapped into data from Electronic Health Records. To make its prediction, the device works by synthesising data about patients like their gender, age and ethnicity.

The same set of vitals was then run through Google's new algorithm, which processed a total of 175,639 data points and predicted a 19.9 percent chance of the patient dying.

Because Google's AI took more into account than the hospital's usual system of assessment, it was able to make a more accurate prediction as a result.

As Bloomberg reports, medical experts have been impressed by Google's ability to dig out data from notes on PDFs or handwritten notes on old charts, which previously have been hard to incorporate into predictive models.

"We demonstrate that deep-learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization", the researchers note.

Google started working on the project with UC San Francisco, the University of Chicago Medicine, and Stanford Medicine previous year, which gave them access to a vast trove of de-identified medical records to validate there deep-learning models.

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"'When will I be able to go home?" She died 10 days after admission. This was significantly better than the hospital's traditional predictive model (the augmented Early Warning Score), which predicted mortality with 85 and 86 percent accuracy respectively.

"These models outperformed traditional, clinically used predictive models in all cases", Rajkomar wrote in Nature.

Google's AI scored 0.86 in predicting if patients would stay long in the hospital, compared to 0.76 using traditional methods.

On inpatient mortality, for example, it scored 0.95 out of a flawless score of 1.0 compared with traditional methods, which scored 0.86. In predicting unexpected readmissions after patients are discharged, Google scored 0.77 compared to traditional methods at 0.70.

Nigam Shah, who is associate professor at Stanford University, was quoted as saying that almost 80 percent of the time spent today in predictive models is for the "scut work", which involves making data presentable.

Google's AI was developed in collaboration with colleagues at UC San Francisco, Stanford Medicine and The University of Chicago Medicine.

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