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Re:An app can transform smartphones into thermometers that a... by h0l0cube on Wednesday June 21, 2023 @07:04PM (#36425939)
> it uses the phone’s touchscreen and repurposes the existing battery temperature sensors to gather data that a machine learning model uses to estimate people’s core body temperatures

> FeverPhone estimated patient core body temperatures with an average error of about 0.41 degrees Fahrenheit (0.23 degrees Celsius), which is in the clinically acceptable range of 0.5 C

Re:An app can transform smartphones into thermometers that a... by nomel on Wednesday June 21, 2023 @09:47PM (#36427094)
> with an average error

Eww, that's not how it works. The goal isn't to make the average near the limit, since the average of -100 and 100 means 0 average error! The goal is to make sure the measurement have a distribution that gives some reasonable confidence that it's within the limit.

This is PTT: https://www.spcpress.com/pdf/DJW244.pdf

Re:An app can transform smartphones into thermometers that a... by glial on Wednesday June 21, 2023 @10:13PM (#36427251)
Abstract says "mean absolute error of 0.229 °C"
Re:An app can transform smartphones into thermometers that a... by srcreigh on Wednesday June 21, 2023 @10:21PM (#36427292)
It’d be hard to get average error so low, since you need precision to get low error values for varying temps.

You can’t just return 100 and -100 randomly, since the actual temp is unknown and you’d need to evenly sample around that.

Re:An app can transform smartphones into thermometers that a... by nomel on Wednesday June 21, 2023 @10:23PM (#36427308)
A normal distribution will average to 0, with enough samples. The center and spread is important, since individual measurements can be considered a random sampling within that distribution. But, this is overly simplifying things, and assume random error, which AI systems often don't exhibit.

It appears this is moot, because the quote did not reflect the actual value or method used.

Re:An app can transform smartphones into thermometers that a... by srcreigh on Wednesday June 21, 2023 @10:31PM (#36427360)
The distribution of human temperature levels is already known, you could just give the average temp as every guess and get maybe 1.5 F average error.

My point is, you can’t really get 0.44F avg error unless it’s predictive. The “std dev” of abs(reality - model) is too high

Re:An app can transform smartphones into thermometers that a... by glibby on Wednesday June 21, 2023 @09:50PM (#36427111)
I would have naively calculated average error as avg(max( abs(smallest), largest)). so it would be 100 in your case. I'm sure that's not statistically correct but it makes sense to me.
Re:An app can transform smartphones into thermometers that a... by Rebelgecko on Wednesday June 21, 2023 @10:36PM (#36427387)
Skimming through the study, it seems like most of the control temps were within a 0.6° C range. I wonder what the average absolute error would be of a thermometer that always returned 37°C
Re:An app can transform smartphones into thermometers that a... by h0l0cube on Thursday June 22, 2023 @07:43PM (#36440794)
According to a sibling comment the abstract claims "a mean absolute error of 0.229 °C". According to wiki the normal temperature window is 36.5–37.5 °C and that of a fever is 37.2-37.7 °C or higher, with variations for both of about 0.5 °C throughout a 24 hour period.

> An early morning temperature higher than 37.2 °C (99.0 °F) or a late afternoon temperature higher than 37.7 °C (99.9 °F) is normally considered a fever, assuming that the temperature is elevated due to a change in the hypothalamus's setpoint.[14] Lower thresholds are sometimes appropriate for elderly people.[14] The normal daily temperature variation is typically 0.5 °C (0.90 °F), but can be greater among people recovering from a fever.

https://en.wikipedia.org/wiki/Human_body_temperature

Re:An app can transform smartphones into thermometers that a... by progrus on Wednesday June 21, 2023 @10:55PM (#36427504)
Amazing!