Machine learning is 85 percent accurate in identifying a person who is suicidal, has a mental illness but is not suicidal, or neither.
Adam Rifkin stashed this in Machine Learning
Also, machine learning is up to 93 percent accurate in correctly classifying a suicidal person.
Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.
I am up to 93 percent accurate at juggling knives ;)
In all seriousness, these are areas where much, much closer to 100% would be good.
Agreed. Is it good to think of this as a start that can be improved?
Or will improvement not come through iterating?
I don't know enough about the problem to answer that. Most of the algorithms technically guarantee improvement the more you train them, but often this comes in the form of asymptotic convergence.
Yeah I got the impression that with more training a system like this could get better.
Reddit comments pointed out that the system could say NO all the time and if in the real world only 1 in 10 is actually a yes then the algorithm is 90% accurate with no training.