Background Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Key PointsQuestionAre results of universal and selective screening for suicide risk implemented in a pediatric emergency department associated with future suicidal behaviors?FindingsIn this cohort study of 15 003 youths aged 8 to 18 years, positive screens were significantly associated with subsequent suicide-related hospital visits compared with standard emergency department procedures. Screening also more than doubled the detection of suicide risk compared with treatment as usual.MeaningThese findings suggest that screening for suicide risk in pediatric emergency departments is an effective approach to identify risk for subsequent suicide-related emergency department visits.

ur research provides preliminary evidence that suicide risk screening is warranted in patients as young as 8-9 years old presenting to the emergency department (ED) with behavioral and mental health symptoms. The goal of this retrospective cohort study (N ¼ 2,466 unique patient visits) was to assess the value of suicide risk screening in children younger than 10 years old who present to the ED with behavioral and mental health concerns. The Johns Hopkins Hospital pediatric ED began screening with the Ask Suicide-Screening Questions (ASQ) for patients 8-21 years old who presented with a behavioral or mental health concern in March 2013 as ED standard of care. We examined the demographic and clinical differences between younger (8-9 years old; n ¼ 270) and older (10-21 years old; n ¼ 2,196) youths who were screened for suicide risk with the ASQ (from March 13, 2013 through December 31, 2016). In summary, 36% of 8-and 9-yearold patients who came to the ED for behavioral and mental health concerns screened positive for suicide risk on the ASQ. The younger patients who screened positive were more likely to present with externalizing symptoms and hallucinations and less likely to present with suicidal ideation or an attempt than their older counterparts. Importantly, 71.1% of 8-to 9-year-old patients who screened positive did not present to the ED for suicidal ideation or attempt vs 50.1% (614/1,226) of patients older than age 10 years.This research is particularly timely and impactful given the 57.4% increase from 2007 to 2018 in the suicide rate among young people 10-24 years old in the United States and among younger subgroups of children: girls 10-14 years old and Black children 5-11 years old. 1,2 The suicide rate of girls 10-14 years old has tripled from 0.5 per 100,000 in 1999 to 1.7 per 100,000 in 2017. 2 ED visit rates among youths with self-inflicted injury, especially in girls 10-14 years old, also increased substantially during a similar time period. 3 The growing literature on preadolescent suicide also reveals stark racial disparities. During the years 2001-2015, Black boys and girls younger than age 13 O

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