Death by Google: Search Engine Giant Creates AI That Is 95% Accurate in Predicting Death of Hospital Patients

Google may one day be able to predict when you’ll die years in advance. The firm has created an AI that it claims is 95 per cent accurate in predicting whether hospital patients will pass away 24 hours after admission. This is around 10 per cent better than traditional models.

“But thou, O Daniel, shut up the words, and seal the book, even to the time of the end: many shall run to and fro, and knowledge shall be increased.” Daniel 12:4 (KJV)

EDITOR’S NOTE: My son Calvin and I had a great talk yesterday while swimming in the ocean, and we discussed how computer technology and AI has radically changed our way of life. So much so that I could not help but think of what the angel Gabriel said to the prophet Daniel about how a sudden and dramatic increase in knowledge is a signal that you are in the end times. Think about it, for roughly the first 5,850 years of human life on this planet, modes of transportation and communication remained virtually unchanged. Then, without warning, in the late 19th century going into the 20th century, it was as if scientists and inventors suddenly had their heads filled with new ideas, with understanding of complex formulas and problem-solving skills showing up virtually all by themselves. How do you explain going from nearly six thousand years of traveling by horse and oxcart to, in the blink of an eye, watching the train, the car, the airplane, the jet engine, and the space ship all showing up in less than 100 years? One answer to that question. 

To make its predictions, the Google software uses data such as patient’s ethnicity, age, gender, previous diagnoses, lab results and vital signs. But what makes it so powerful is that it includes data previously thought out of reach of machines, such as doctor notes buried in PDFs or scribbled on old charts.

As well as death, AI can also predict unplanned readmissions within 30 days and  probable length of stay at a hospital.  The system is still in its infancy, but Google believes it could someday be used to predict death far longer in advance.

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

To test the system, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them. After studying the data, the AI was able to identify which words were associated closest with outcomes.

While the results have not been validated, Google claims huge improvements over traditional models. The biggest benefit, researchers claim, is the ability for the system to use all types of data. Nigam Shah, an associate professor at Stanford University told Bloomberg that as much as 80 per cent of the time spent on predictive models goes to making the data presentable. ‘With Google you can throw in the kitchen sink and not have to worry about it,’ Shah said.

In a written statement, Google research scientist and medical doctor Alvin Rajkomar MD said: ‘When patients get admitted to a hospital, they have many questions about what will happen next. When will I be able to go home? Will I get better? Will I have to come back to the hospital? Predicting what will happen next is a natural application of machine learning.’

Google’s tool can predict a range of patient outcomes, including the length of a patient’s stay in hospital as well as their chances of readmission.

For each prediction, a deep learning model reads all the data-points in electronic health records, from earliest to most recent, and then learns which data helps to predict the outcome. The final results showed impressive accuracy.

Where 1.00 is perfect, and 0.50 is no better than random chance, Google’s AI scored the following: 0.86 in predicting if patients would stay long in the hospital, compared to 0.76 using traditional methods; 0.95 in predicting inpatient mortality, compared to traditional methods at 0.86; they 0.77 in predicting unexpected readmissions after patients are discharged, compared to traditional methods at 0.70.

The full findings of the study were published in the Nature partner journal Digital Medicine. source

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