Machine learning of neural representations of emotion identifies suicidal youth
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Re: Machine learning of neural representations of emotion identifies suicidal youth
#2Maybe they should use this test before gun purchases... I don't think someone suicidal should purchase a gun...hell I don't care if they kill themselves, but lately a lot of suicides were mass suicides...we don't need more of that shit.
Re: Machine learning of neural representations of emotion identifies suicidal youth
#3Will it be that 10% of people are suicidal and it always predicts non-suicidal?
Will it be that accuracy actually means AUC?
Will it be that they are reporting predictive skill on the training data?
Re: Machine learning of neural representations of emotion identifies suicidal youth
#4"This study used machine-learning algorithms (Gaussian Naive Bayes) to identify such individuals (17 suicidal ideators versus 17 controls) with high (91%) accuracy, based on their altered functional magnetic resonance imaging neural signatures of death-related and life-related concepts."
Anyone with a Nature subscription want to check whether they simply trained their discriminator and then used it on the same data set? There's no mention in the abstract of testing it against a fresh control set and that's not promising.
https://www.nature.com/articles/s41562-017-0234-y?error=cook...
Re: Machine learning of neural representations of emotion identifies suicidal youth
#5To the moderators, the title would be more accurate with 'fMRI' as opposed to 'MRI'. The latter is typically used to examine structural brain elements, whereas fMRI is thought to correlate with brain activity and, by extension, thought.
Confusing the two would lead to the more unusual conclusion that suicidal ideation is associated with abnormal brain connectivity, while the authors are instead focusing on neuronal activity.
Re: Machine learning of neural representations of emotion identifies suicidal youth
#6So what will they do after they detect you are suicidal? Stick you in a psych ward? Yet more attempts at taking away the rights of those going through trauma.
Re: Machine learning of neural representations of emotion identifies suicidal youth
#7Slightly off topic but the book "Change your brain change your life" was pretty interesting. Perhaps not as scientific as some would prefer, but none the less thought provoking.
Re: Machine learning of neural representations of emotion identifies suicidal youth
#8Maybe they should use this test before gun purchases... I don't think someone suicidal should purchase a gun...hell I don't care if they kill themselves, but lately a lot of suicides were mass suicides...we don't need more of that shit.
Problem is people buy guns when they aren't suicidal.
Tip: If you own a gun and are feeling suicidal, give it to a trusted person for safekeeping.
Re: Machine learning of neural representations of emotion identifies suicidal youth
#9Maybe they should use this test before gun purchases... I don't think someone suicidal should purchase a gun...hell I don't care if they kill themselves, but lately a lot of suicides were mass suicides...we don't need more of that shit.
Problem is people buy guns when they aren't suicidal. Tip: If you own a gun and are feeling suicidal, give it to a trusted person for safekeeping.
It can't hurt to get rid of the gun if you're suicidal. But would it actually make a difference? Suicide rates across countries aren't related to gun availability.
Re: Machine learning of neural representations of emotion identifies suicidal youth
#10"This study used machine-learning algorithms (Gaussian Naive Bayes) to identify such individuals (17 suicidal ideators versus 17 controls) with high (91%) accuracy, based on their altered functional magnetic resonance imaging neural signatures of death-related and life-related concepts." Anyone with a Nature subscription want to check whether they simply trained their discriminator and then used it on the same data s…
scihub has it. looks like leave-one-out cross validation?
"A Gaussian Naive Bayes (GNB) classifier trained on the data of 33 out of 34 participants predicted the group membership of the remaining participant with a high accuracy of 0.91 (P<0.000001), correctly identifying 15 of the 17 suicidal participants and 16 of the 17 controls"