AI can predict when someone will die with unsettling accuracy. - NBCNewsMACH
To evaluate the likelihood of subjects' premature mortality, the researchers tested two types of AI:"deep learning," in which layered information-processing networks help a computer to learn from examples; and"random forest," a simpler type of AI that combines multiple, tree-like models to consider possible outcomes.
Then, they compared the AI models' conclusions to results from a standard algorithm, known as the Cox model.Using these three models, the scientists evaluated data in the UK Biobank — an open-access database of genetic, physical and health data — submitted by more than 500,000 people between 2006 and 2016. During that time, nearly 14,500 of the participants died, primarily from cancer, heart disease and respiratory diseases.
The Cox model leaned heavily on ethnicity and physical activity, while the machine-learning models did not. By comparison, the random forest model placed greater emphasis on, waist circumference, the amount of fruit and vegetables that people ate, and skin tone, according to the study. For the deep-learning model, top factors included exposure to job-related hazards and air pollution, alcohol intake and the use of certain medications.
When all the number crunching was done, the deep-learning algorithm delivered the most accurate predictions, correctly identifying 76 percent of subjects who died during the study period. By comparison, the random forest model correctly predicted about 64 percent of premature deaths, while the Cox model identified only about 44 percent.
This isn't the first time that experts have harnessed AI's predictive power for health care. In 2017, a different team of researchers demonstrated that AI could learn to spot early signs of Alzheimer's disease; their algorithm evaluated brain scans to predict if a person would be likely to develop Alzheimers, and it did so with about 84 percent accuracy, Live Science
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