A supervised learning algorithm can predict clinical depression much earlier and more accurately than trained health professionals.
One of the more surprising, and upsetting, uses of social media has been suicides performed on Facebook Live. Though reasons for suicide are complex, the mere threat is often a cry for help, acceptance, or recognition. During the two years I worked as a patient monitor in an emergency room, I discovered most people that attempt to take their own lives desire a pair of ears to listen to their problems more than anything else.
It's hard to gauge a person's reality based on social media habits, however. Those who spout vitriolic rhetoric are often quite approachable and reserved in person. We can't read inflections and temperament from words on a screen, or take into consideration that the person might just be having a bad day.
That said, social media can be a powerful indicator of those at risk for suffering from mental health disorders, a new study published in Scientific Reports suggests. A team led by Andrew Reece, in the Department of Psychology at Harvard, collected Twitter data from 204 individuals. Of those, 105 suffered from depression, with a control of 99 healthy subjects. The team then used a supervised learning algorithm to see if changes in language predicted clinical depression.
The answer is yes. Depressed patients used more words like death, no, and never, while posting fewer positive words—like happy, beach, and photo—in the lead-up to their diagnosis.
Figure 4. Depression word-shift graph revealing contributions to difference in Twitter happiness observed between depressed (5.98) and healthy (6.11) participants. In column 3, (−) indicates a relatively negative word, and (+) indicates a relatively positive word, both with respect to the average happiness of all healthy tweets. An up (down) arrow indicates that word was used more (less) by the depressed class. Words on the left (right) contribute to a decrease (increase) in happiness in the depressed class. [Credit: Andrew G. Reece et. al.]
A second group pf 174 Twitter users was also studied. Of these, 63 suffered from PTSD. Again, changes in language revealed that they were likely to be diagnosed.
These results are not perfect. In both situations there was a preselected pool of Twitter users with a close ratio of healthy to unhealthy, which does not reflect society as a whole. Add to this fact that many depressed people or those suffering from PTSD do not use social media. It would be hard to acquire firm numbers based on these shortcomings.
That said, Reece and his team are borrowing this predictive model from similar early warning systems in place for hard-to-detect cancers, disease outbreaks, and regional dietary health issues. Diseases like addiction and suicidal ideation have already been studied through social media. While this trend of using public facing data to detect potential cognitive disorders is new, cries for help might be detected, and treated, much sooner.
Reece and team believe they have found if not a silver bullet for predicting depression and PTSD, at least a shinier one than has so far been developed:
Our findings strongly support the claim that computational methods can effectively screen Twitter data for indicators of depression and PTSD. Our method identified these mental health conditions earlier and more accurately than the performance of trained health professionals, and was more precise than previous computational approaches.
With the current rise in depression and anxiety, especially among teens, a particularly vulnerable group that has now fully grown up on social media, such predictive tools could prove to be a valuable source of therapy and recovery moving forward.
"We hope that our research will eventually help improve mental health care, for example in preventive screening," Stanford researcher Katharina Lix told Digital Trends. “We could imagine clinicians using this technology as a supporting tool during a patient's initial assessment, provided that the patient has agreed to have their social media data used in this way. However, before we get to that point, the technology needs to be validated using a larger sample of people that's representative of the general population. We want to emphasize that any real-world application of this technology must carefully take into account ethical and privacy concerns."
Derek is the author of Whole Motion: Training Your Brain and Body For Optimal Health. Based in Los Angeles, he is working on a new book about spiritual consumerism. Stay in touch on Facebook and Twitter.
Scientists create a portable device that can detect 17 diseases, including 8 different cancers, straight from a person's breath.
Scientists have created a device straight out of Star Trek that can detect 17 diseases, including 8 different types of cancer, just from your breath. The tricorder-like Na-nose can spot chemical signatures of the diseases and it’s hoped it will revolutionize treatment of many dangerous illnesses by spreading convenient early-detection technology. The international team of researchers from 5 countries, led by Professor Hassam Haick of the Technion-Israel Institute of Technology, is next developing Sniffphone for disease detection right through your smartphone.
The Na-nose device features a sensor nano-array of carbon nanotubes and tiny gold particles controlled by AI software. This program can analyze human breath samples for special chemical signatures that correspond to various diseases. This works because people exhale over a 100 unique chemicals called volatile organic compounds (VOCs) and the team proved that each disease has a very specific chemical signature within a person’s VOC. The scientists used mass spectrometry to figure out a 13-component "breathprint" for each of the 17 diseases in the study.
"We found that just as we each have a unique fingerprint, each of the diseases we studied has an unique breath print, a 'signature' of chemical components," said Professor Haick. "We have a device which can discriminate between them, which is elegant and affordable."
Why is breath particularly convenient for diagnosis?
“Breath is an excellent raw material for diagnosis,” Professor Haick told Haaretz. “It is available without the need for invasive and unpleasant procedures, it’s not dangerous, and you can sample it again and again if necessary.”
Besides cancers, the conditions the device can diagnose include Parkinson’s, multiple sclerosis. Crohn’s disease and kidney disease. The Na-nose was tested on 2,800 breath samples from 1,404 people in the U.S., Israel, France, Latvia and China and was able to correctly diagnose in almost 9 out 10 cases.
It’s the first time a device was created that can distinguish between different diseases in a breath sample. Artificial intelligence plays a large role in that. Professor Haick, a nanotechnology expert, explained its workings this way to Smithsonian.com:
“We can teach the system that a breathprint could be associated with a particular disease,” said Haick. “It works in the same way we'd use dogs in order to detect specific compounds. We bring something to the nose of a dog, and the dog will transfer that chemical mixture to an electrical signature and provide it to the brain, and then memorize it in specific regions of the brain … This is exactly what we do. We let it smell a given disease but instead of a nose we use chemical sensors, and instead of the brain we use the algorithms. Then in the future, it can recognize the disease as a dog might recognize a scent.”
Haick said their AI “nose” can be used in other industries as well, like security or quality control.
If you’re looking for historical perspective, even ancient doctors as far back as the famous Greek physician Hippocrates (460-370 BC) were used to smelling the breaths of their patients to figure out their illnesses.
The scientists have continued testing the device on thousands more patients since the trial and hope to bring it to market soon. They think making this technology widespread could really impact the survival rates of patients with certain diseases by allowing for much earlier detection.
You can read the study here, in the American Chemical Society Nano journal.
Watch the interview with Professor Haick here:
Cover photo: Na-nose device. Credit: Technion-Israel Institute of Technology/Youtube.