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Children who struggle with memory issues and have a poor attention span are more likely to develop mental health conditions when they become adults, study shows. Researchers from the University of Birmingham analysed data from a cohort of 13,988 individuals born in 1991 and 1992 and re-examined over decades. They set out to look for any links between childhood cognitive problems such as lack of control and memory issues, and later life mental health conditions. They found that poor attention span in eight year olds could lead to depression at 18, and memory problems at ten could lead to hypomania when they are 22 years old. Targeting specific markers in childhood for early treatment may help to minimise the risk of developing certain psychopathological problems later in life.

Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, getting actionable information from these rather complex time series is challenging, and at present the implications for clinical care are largely hypothetical. This research demonstrates that, with well chosen cohorts (of bipolar disorder, borderline personality disorder, and control) and modern methods, it is possible to objectively learn to identify distinctive behaviour over short periods (20 reports) that effectively separate the cohorts. Participants with bipolar disorder or borderline personality disorder and healthy volunteers completed daily mood ratings using a bespoke smartphone app for up to a year. A signature-based machine learning model was used to classify participants on the basis of the interrelationship between the different mood items assessed and to predict subsequent mood. The signature methodology was significantly superior to earlier statistical approaches applied to this data in distinguishing the participant three groups, clearly placing 75% into their original groups on the basis of their reports. Subsequent mood ratings were correctly predicted with greater than 70% accuracy in all groups. Prediction of mood was most accurate in healthy volunteers (89-98%) compared to bipolar disorder (82-90%) and borderline personality disorder (70-78%).

Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively assessing human behavior and comparing it with a well-established norm. Just as tricky, there are few well-founded models of neural circuitry or brain biochemistry, and it is difficult to link this science with real-world behavior. That has begun to change thanks to the emerging discipline of computational psychiatry, which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors.

Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively assessing human behavior and comparing it with a well-established norm. Just as tricky, there are few well-founded models of neural circuitry or brain biochemistry, and it is difficult to link this science with real-world behavior. That has begun to change thanks to the emerging discipline of computational psychiatry, which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors.