Psychological trauma can manifest following various distressing events and is captured in diverse online contexts. However, studies traditionally focus on a single aspect of trauma, often neglecting the transferability of findings across different scenarios. We address this gap by training language models with progressing complexity on trauma-related datasets, including genocide-related court data, a Reddit dataset on post-traumatic stress disorder (PTSD), counseling conversations, and Incel forum posts. Our results show that the fine-tuned RoBERTa model excels in predicting traumatic events across domains, slightly outperforming large language models like GPT-4. Additionally, SLALOM-feature scores and conceptual explanations effectively differentiate and cluster trauma-related language, highlighting different trauma aspects and identifying sexual abuse and experiences related to death as a common traumatic event across all datasets. This transferability is crucial as it allows for the development of tools to enhance trauma detection and intervention in diverse populations and settings.

Developer Kevin Kim lived through Hurricane Sandy in 2012 and saw the deep impact the storm had on close friends -- not only on physical belongings and health, but on their mental well-being. One of Kim's friends, for example, had a tree crash through his roof, and though no one was physically hurt, dealing with the insurance and finances after the storm took a heavy toll on his friend. The experience spurred Kim and his teammates -- Christopher McKinney, Sunjae Shim, Tony Park, and Xuelong Mu -- to join forces and create Healios, an online platform that uses a conversational interface and AI to help connect those who need mental healthcare with the right case worker. The team's solution has been named a top-five finalist in the Call for Code 2019 Global Challenge. "There are a lot of stressors that are being constantly inundated with needs that pop out of nowhere, that they never had to deal with," Kim said.

This article is part 1 of a series sharing the initial results and directions of Omdena's AI challenge on PTSD treatment with 34 engineers and enthusiasts collaborating. Millions of people suffer from PTSD around the world due to various kinds of traumatic events. Professional help is difficult to find when needed in particular areas, which makes it sometimes impossible for the patient to overcome the trauma they live in. This is why Christoph von Toggenburg reached out to Omdena for leverage AI and community collaboration. Before we dive in deeper let's first understand what PTSD is and how professional psychiatrists treat it.

The symptoms of post-traumatic stress disorder include uncontrolled memories of a traumatic event, anxiety and panic--"hyperarousal" is the technical term--depression, avoiding anything that's a reminder of the event, self-destructive behavior, and more. It's the only psychiatric disorder where people are pretty sure of the cause: emotionally traumatic events, from the death of a loved one to an experience of fear or violence. By some estimates 5 to 10 percent of all US adults have PTSD, more women than men. Wars in Afghanistan, Iraq, and other places US troops are deployed put those numbers even higher among people in the military and veterans. But the biology of PTSD--neurological changes, elevated or depressed levels of something a blood test could pick up, genetic vulnerabilities--is … multifactorial.

Memories of bad events are painful to think about by their very nature, and traumatic events can often seem worse than they really were when you relive them. This is because we are less likely to remember the context surrounding a bad experience, only the experience itself, according to research out today. The findings could help explain why some therapies for conditions such as post-traumatic stress disorder work and might lead to new way to treat sufferers. Memories of bad events are painful to think about by their very nature, and traumatic events can often seem worse than they really were when you relive them. A research group at University College London placed 20 volunteers in an MRI scanner and showed them pairs of pictures, some of which included negative content.