Linguistic red flags from Facebook posts can predict future depression diagnoses — ScienceDaily
▻https://www.sciencedaily.com/releases/2018/10/181015150643.htm
Research finds that the language people use in their Facebook posts can predict a future diagnosis of depression as accurately as the tools clinicians use in medical settings to screen for the disease.
In any given year, depression affects more than 6 percent of the adult population in the United States — some 16 million people — but fewer than half receive the treatment they need. What if an algorithm could scan social media and point to linguistic red flags of the disease before a formal medical diagnosis had been made?
Ah oui, ce serait fantastique pour les Big Pharma : la dépression est une maladie complexe, dont les symptômes graves sont souvent confondus avec la déprime qui est un état sychologique que nous connaissons tous. Notre Facebook, couplé avec notre assistant vocal Amazon nous gorgerait de Valium, et tout irait pour le mieux dans le Meilleur des mondes.
Considering conditions such as depression, anxiety, and PTSD , for example, you find more signals in the way people express themselves digitally."
For six years, the WWBP, based in Penn’s Positive Psychology Center and Stony Brook’s Human Language Analysis Lab, has been studying how the words people use reflect inner feelings and contentedness. In 2014, Johannes Eichstaedt, WWBP founding research scientist, started to wonder whether it was possible for social media to predict mental health outcomes, particularly for depression.
“Social media data contain markers akin to the genome,” Eichstaedt explains. “With surprisingly similar methods to those used in genomics, we can comb social media data to find these markers. Depression appears to be something quite detectable in this way; it really changes people’s use of social media in a way that something like skin disease or diabetes doesn’t.”
Il y a au moins une bonne nouvelle sur la déontologie scientifique :
Rather than do what previous studies had done — recruit participants who self-reported depression — the researchers identified data from people consenting to share Facebook statuses and electronic medical-record information, and then analyzed the statuses using machine-learning techniques to distinguish those with a formal depression diagnosis.
Les marqueurs considérés sont aussi des marqueurs sociaux et économiques, qu’il faudrait traiter autrement qu’avec des médicaments.
They learned that these markers comprised emotional, cognitive, and interpersonal processes such as hostility and loneliness, sadness and rumination, and that they could predict future depression as early as three months before first documentation of the illness in a medical record.
La conclusion est fantastique : il faut rendre le balayage obligatoire !!!
Eichstaedt sees long-term potential in using these data as a form of unobtrusive screening. “The hope is that one day, these screening systems can be integrated into systems of care,” he says. “This tool raises yellow flags; eventually the hope is that you could directly funnel people it identifies into scalable treatment modalities.”
Despite some limitations to the study, including its strictly urban sample, and limitations in the field itself — not every depression diagnosis in a medical record meets the gold standard that structured clinical interviews provide, for example — the findings offer a potential new way to uncover and get help for those suffering from depression.
#Dépression #Facebook #Foutaises #Hubris_scientifique #Big_pharma #Psychologie
