The Centers for Disease Control and Prevention (CDC) is using data from platforms like Reddit and Twitter to power artificial intelligence that can forecast suicide rates. The agency is doing this because its current suicide statistics are delayed by up to two years, which means that officials are forming policy and allocating mental health resources throughout the country without the most up-to-date numbers. The CDC's suicide rate statistics are calculated based on cause-of-death reports from throughout the 50 states, which are compiled into a national database. That information is the most accurate reporting we have, but it can take a long time to produce. "If we want to do any kind of policy change, intervention, budget allocation, we need to know the real picture of what is going on in the world in terms of people's mental health experiences," Munmun de Choudhury, a professor at Georgia Tech's School of Interactive Computing who is working with the CDC, told Recode.
"What's the problem you're trying to solve?" Clayton Christensen, the late Harvard business professor, was famous for posing this aphoristic question to aspiring entrepreneurs. By asking it, he was teaching those in earshot an important lesson: Innovation, alone, isn't the end goal. To succeed, ideas and products must address fundamental human problems. This is especially true in healthcare, where artificial intelligence is fueling the hopes of an industry desperate for better solutions. But here's the problem: Tech companies too often set out to create AI innovations they can sell, rather than trying to understand the problems doctors and patients need solved.
A US research team has developed a computer that can accurately predict whether an antidepressant will work, based on patients' brain activity. The multi-site trial initiated by UT Southwestern in 2011 to better understand mood disorders -- involving Stanford, Harvard and other institutions -- demonstrates that artificial intelligence (AI) may soon help doctors objectively diagnose and prescribe depression treatments. The researchers predict that tools such as AI, brain imaging and blood tests will revolutionise the field of psychiatry in the coming years. "These studies have been a bigger success than anyone on our team could have imagined," UT Southwestern psychiatrist Dr Madhukar Trivedi said. "We provided abundant data to show we can move past the guessing game of choosing depression treatments and alter the mindset of how the disease should be diagnosed and treated."
A new study finds that an interactive voice application using artificial intelligence is an effective way to monitor the well-being of people being treated for serious mental illness. Researchers from UCLA followed 47 people for up to 14 months using an application called MyCoachConnect. The data were collected from 2013 and 2015. All of the patients were being treated by physicians for serious mental illnesses, including bipolar disorder, schizophrenia and major depressive disorder. For the study, published in PLOS One, participants called a toll-free number one or two times a week and answered three open-ended questions when prompted by a computer-generated voice.
In neuropsychiatric disorders such as schizophrenia or depression, there is often a disruption in the way that regions of the brain synchronize with one another. To facilitate understanding of network-level synchronization between brain regions, we introduce a novel model of multisite low-frequency neural recordings, such as local field potentials (LFPs) and electroencephalograms (EEGs). The proposed model, named Cross-Spectral Factor Analysis (CSFA), breaks the observed signal into factors defined by unique spatio-spectral properties. These properties are granted to the factors via a Gaussian process formulation in a multiple kernel learning framework. In this way, the LFP signals can be mapped to a lower dimensional space in a way that retains information of relevance to neuroscientists.
Schizophrenia is a complex psychiatric disorder that has eluded a characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, emergent working of the brain. We propose a novel data-driven approach to capture emergent features using functional brain networks [Eguiluzet al] extracted from fMRI data, and demonstrate its advantage over traditional region-of-interest (ROI) and local, task-specific linear activation analyzes. Our results suggest that schizophrenia is indeed associated with disruption of global, emergent brain properties related to its functioning as a network, which cannot be explained by alteration of local activation patterns. Moreover, further exploitation of interactions by sparse Markov Random Field classifiers shows clear gain over linear methods, such as Gaussian Naive Bayes and SVM, allowing to reach 86% accuracy (over 50% baseline - random guess), which is quite remarkable given that it is based on a single fMRI experiment using a simple auditory task. Papers published at the Neural Information Processing Systems Conference.
Work-related mental health has become a pressing issue for businesses globally. In the UK, a government-backed Labour Force Survey found that the total number of cases of work-related stress, depression, or anxiety in 2018 and 2019 was 602,000, resulting in a loss of 12.8 million working days. A recent WHO-led study also estimated that depression and anxiety disorders are costing the global economy $1 trillion USD each year in lost productivity. The good news is that artificial intelligence (AI) and machine-based learning have the potential to help in the workplace significantly, once they truly come of age. In its current form, AI is primarily a support mechanism.
In a world first, a medicine developed by artificial intelligence may be used to treat patients with obsessive-compulsive disorder. The news is remarkable and hints that in the future, AI may help drug development become faster and more efficiently than ever before. The first non-man made drug molecule, DSP-1181, has now entered Phase 1 clinical trials, European Pharmaceutical Review reported. The molecule is a long-acting potent serotonin 5-HT1A receptor agonist and was developed using AI that was the product of a partnership between Japan's Sumitomo Dainippon Pharma and Exscientia in the UK. The compound was developed in a remarkable time, with AI able to complete in 12 months what typically takes five years.
An AI can predict from people's brainwaves whether an antidepressant is likely to help them. The technique may offer a new approach to prescribing medicines for mental illnesses. Antidepressants don't always work, and we aren't sure why. "We have a central problem in psychiatry because we characterise diseases by their end point, such as what behaviours they cause," says Amit Etkin at Stanford University in California. "You tell me you're depressed, and I don't know any more than that. I don't really know what's going on in the brain and we prescribe medication on very little information."