Mental illnesses diagnosable by AI focused on Facebook
Drawing on nothing more than Facebook activity, psychiatric AI can distinguish individuals headed for hospitalization with schizophrenia from those with worsening mood disorders such as clinical depression and bipolar states, according to a study published Dec. 3 in NPJ Schizophrenia.
The performance of the tested algorithms was impressive enough that the study authors, from the Feinstein Institutes in New York and IBM Research, suggest the technique be integrated with other patient-specific information to guide clinical care paths.
Michael Birnbaum, MD, and colleagues gathered more than 3.4 million Facebook messages and more than 140,000 images posted by 223 participants recruited from the psychiatry department at Feinstein-affiliated Northwell Health.
The group ranged in age from 15 to 35 and included 79 patients with a schizophrenia spectrum disorder (SSD), 74 with a mood disorder (MD) and 70 healthy volunteers (HVs).
The researchers used machine learning to build classifiers for distinguishing between the three psychiatric statuses, analyzing features the participants posted up to a year and a half before their first hospitalization.
The AI classifiers had high accuracy telling HV from MD, HV from SSD and SSD from MD.
“While Facebook alone is not meant to diagnose psychiatric conditions or to replace the critical role of a clinician in psychiatric assessment, our results suggest that social media data could potentially be used in conjunction with clinician information to support clinical decision-making,” Birnbaum et al. comment in their discussion. “Much like an X-ray or blood test is used to inform health status, Facebook data, and the insights we gather, could one day serve to provide additional collateral, clinically meaningful patient information.”
A news release sent by Northwell Health highlights some of the project’s more fascinating sub-findings:
- SSD and MD participants were more likely to use swear words on Facebook in comparison to HV;
- SSD members used more perception words—like “hear,” “see” and “feel,” than MD or HV;
- The MD cohort used more words related to blood, pain and other biological processes;
- Closer to hospitalization, punctuation increased in SSD compared to HV; and
- The use of negative emotion words increased in MD compared to HV.
What’s more, the height and width of photos posted to Facebook by participants with schizophrenic and mood disorders were smaller than those posted by the healthy volunteers. Also, the photos uploaded by those with mood disorders contained more blues and fewer yellows.
“There is great promise in the current research regarding the relationship between social media activity and behavioral health, and our results … demonstrate that machine learning algorithms are capable of identifying signals associated with mental illness, well over a year in advance of the first psychiatric hospitalization,” Birnbaum tells Northwell’s news division. “We have the potential to thoughtfully bring psychiatry into the modern, digital age by integrating these data into the field.”
The study is available in full for free.
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.