Machine-Learning Identifies Heart Arrhythmia

It might not be long before algorithms routinely save lives—as long as doctors are willing to put ever more trust in machines. A team of researchers at Stanford University, led by Andrew Ng, has shown that a machine-learning model can identify heart arrhythmias from an electrocardiogram (ECG) better than an expert. The automated approach could prove important to everyday medical treatment by making the diagnosis of potentially deadly heartbeat irregularities more reliable. It could also make quality care more readily available in areas where resources are scarce. Read more

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