This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Firefighters represent an important population for understanding the consequences of exposure to potentially traumatic stressors.

The researchers were interested in the effects of pre-employment disaster exposure on firefighter recruits’ depression and posttraumatic stress disorder (PTSD) symptoms during the first three years of fire service and hypothesized that: (1) disaster-exposed firefighters would have greater depression and PTSD symptoms than non-exposed overall; and (2) depression and PTSD symptoms would worsen over years in fire service in exposed firefighters, but not in their unexposed counterparts.

In a baseline interview, 35 male firefighter recruits from seven US cities reported lifetime exposure to natural disaster. These disaster-exposed male firefighter recruits were matched on age, city, and education with non-exposed recruits.

A generalized linear mixed model revealed a significant exposure×time interaction (ecoef =1.04; P<.001), such that depression symptoms increased with time for those with pre-employment disaster exposure only. This pattern persisted after controlling for social support from colleagues (ecoefficient=1.05; P<.001), social support from families (ecoefficient=1.04; P=.001), and on-the-job trauma exposure (coefficient=0.06; ecoefficient=1.11; P<.001). Posttraumatic stress disorder symptoms did not vary significantly between exposure groups at baseline (P=.61).

Depression symptoms increased with time for those with pre-employment disaster exposure only, even after controlling for social support. Posttraumatic stress disorder symptoms did not vary between exposure groups.

PenningtonML , CarpenterTP , SynettSJ , TorresVA , TeagueJ , MorissetteSB , KnightJ , KamholzBW , KeaneTM , ZimeringRT , GulliverSB . The Influence of Exposure to Natural Disasters on Depression and PTSD Symptoms among Firefighters. Prehosp Disaster Med. 2018;33(1):102–108.