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An algorithm is spotting heart problems better than an expert doctor

An algorithm is spotting heart problems better than an expert doctor | healthcare technology | Scoop.it

It might not be long before algorithms routinely save lives—as long as doctors are willing to put ever more trust in machines.

 

An algorithm that spots heart arrhythmia shows how AI will revolutionize medicine—but patients must trust machines with their lives.

 

A team of researchers at Stanford University, led by Andrew Ng, a prominent AI researcher and an adjunct professor there, 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.

 

The work is also just the latest sign of how machine learning seems likely to revolutionize medicine. In recent years, researchers have shown that machine-learning techniques can be used to spot all sorts of ailments, including, for example, breast cancer, skin cancer, and eye disease from medical images.

 

more at : https://www.technologyreview.com/s/608234/the-machines-are-getting-ready-to-play-doctor/

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Fitness Trackers Are Useless Without Real-Time, Personalized Analysis

Fitness Trackers Are Useless Without Real-Time, Personalized Analysis | healthcare technology | Scoop.it

No one has arms long enough to wear all of the activity-tracking wristbands currently on sale or awaiting release. These devices count your steps, measure your sleep and some even monitor your heart rate.


But do you know how this information immediately applies to your lifestyle, or what you should do with it?


The services behind these trackers need to invest in immediacy by providing useful information, ideally in real time, so we can optimize our wealth of data into action.


Everyone wants to be better, but nobody has a baseline for understanding themselves.


what use is the data without knowing in real time what you, individually, can do to change it?


I’d like to know whether I need to slow down. Am I pushing myself too hard?

nrip's insight:

If I got a dollar for each time I said this to someone in the last year, I would have got a million plus by now :) ...  I am happy that others see this as a deal breaker for wearables too.


The mediXcel PHR is solving this very problem by trying to build a personalized analysis engine on top of the wearable databank it has which connects to 40 odd wearables at the moment.

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Jay Gadani's curator insight, August 6, 2014 11:46 PM

A good example of how data is cool. But, in order to make it meaningful, it needs to be analysis!! 

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