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Abstract Background Suicide attempts (SA) constitute a serious clinical problem. People who attempt suicide are at high risk of further repetition. However, no interventions have been shown to be effective in reducing repetition in this group of patients. Methods/Design Multicentre randomized controlled trial. We examine the effectiveness of «ALGOS algorithm»: an intervention based in a decisional tree of contact type which aims at reducing the incidence of repeated suicide attempt during 6 months. This algorithm of case management comprises the two strategies of intervention that showed a significant reduction in the number of SA repeaters: systematic telephone contact (ineffective in first-attempters) and «Crisis card» (effective only in first-attempters). Participants who are lost from contact and those refusing healthcare, can then benefit from «short letters» or «postcards». Discussion ALGOS algorithm is easily reproducible and inexpensive intervention that will supply the guidelines for assessment and management of a population sometimes in difficulties with healthcare compliance. Furthermore, it will target some of these subgroups of patients by providing specific interventions for optimizing the benefits of case management strategy. Trial Registration The study was registered with the ClinicalTrials.gov Registry; number: NCT01123174.

Introduction: Early‐onset psychotic disorders include the prodromal phase and the first‐episode psychosis (FEP). They constitute a high‐risk period for suicidal behavior. Early intervention for psychosis (EIP) consists of intervening as early as possible. The effectiveness of early intervention on overall prognosis has been reported in numerous studies, and EIP services are emerging worldwide. Several authors report an improvement in suicidal behavior, but no study has looked at all the data. Aims of the Study: The aim of work is to study whether early intervention for psychosis has an impact on deaths by suicide and suicide attempts, and study which intervention methods have an impact on suicidal behavior. Methodology: By respecting the PRISMA criteria, previously declared on PROSPERO, by exploring 5 medical databases (PubMed, Cochrane, PsycINFO, Scopus, Embase), from their creation dates, published until 20/02/2023, in English, we carried out a meta‐analysis. The articles selected had to deal with the EIP and deaths by suicide or suicide attempts. Our primary outcome is the deaths by suicide and the secondary outcome the suicide attempt. Results: The exhaustive search identified a total of 2310 references. Nine articles were included. Their intervention modalities were pharmacotherapy, psychotherapy, case‐management, or related services, and psycho‐social therapies. Our meta‐analysis shows that early intervention for early‐onset psychotic disorders is associated with a statistically significant reduction by a third in deaths by suicide (ORa = 0.66 (0.49–0.88), p = 0.005) and by a third in suicide attempts (ORa = 0.66 (0.50–0.86), p = 0.002), with non‐significant heterogeneity. Sensitivity analyses excluding the study with statistical difficulties due to the absence of an event and studies with a high risk of bias point in the same direction, that is a statistically significant reduction and non‐significant heterogeneity. Conclusion: The literature shows that early intervention programs are associated with positive impact on deaths by suicide and suicide attempt. This is the first meta‐analysis of early intervention in early psychotic disorders and its impact on suicidal risk. The deployment of EIP should be supported worldwide in order to intervene as early as possible and prevent the risk of suicide. Trial Registration: PROSPERO CRD42022366976 [ABSTRACT FROM AUTHOR]

Introduction: Bipolar disorders (BD) are characterized by mood symptoms that can worsen medication side effects. We aimed to study the association between residual mood signs and self‐reported side effects in the euthymic phase of BD. Methods: We assessed residual mood signs using the Montgomery–Asberg Depression Rating scale (MADRS) and Young Mania Rating scale (YMRS) and self‐reported side effects using the Patient‐Rated Inventory of Side Effects (PRISE‐M) for 880 males and 1369 females with BD. We conducted a network analysis to test the associations between 52 items of the three scales for males and females separately. We then identified clusters of nodes that fit the networks well. Results: We report only positive associations between residual mood signs and side effects. An elevated mood (YMRS) in females and increased energy (YMRS) in males were central nodes, strongly influencing the development of additional mood symptoms and side effects. Furthermore, we identified three clusters of nodes in both sexes: (1) a "mood cluster", including most YMRS and MADRS items and the PRISE‐M items evaluating sedation, sleep, and restlessness, (2) a cluster of nonsexual side effects (mostly PRISE‐M items), and (3) a cluster of sexual side effects. In both sexes, we identified bridge nodes that may favor the communication between mood and side effects, namely palpitations (PRISE‐M) and agitation (PRISE‐M). Conclusions: The results justify the particular attention of practitioners to monitor elevated moods or increased energy to try to reduce self‐reported side effects and other residual mood symptoms in the euthymic phase of BD. Our findings suggest that clinicians could consider patient‐reported loss of energy, difficulty in falling asleep, and restlessness as mood symptoms rather than medications' side effects. Palpitations and agitation may contribute to the development of additional mood symptoms or somatic complaints. [ABSTRACT FROM AUTHOR]

BackgroundMachine 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. ObjectiveThe 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 MethodsThis 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. ResultsA 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. ConclusionsMachine 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.

Background: The early stages of psychotic disorders correspond to the early phases of the disease and include the prodromal phase and first-episode psychosis; they constitute a period at high risk of suicidal behaviour. A long duration of untreated psychosis (DUP) is among the risk factors of suicidal behaviour identified in this early period. Many studies have shown the effectiveness of early interventions on the overall prognosis of psychotic disorders in the early stages, and early intervention strategies have been developed and tested worldwide. Several authors reported an improvement in suicidal behaviours; however, all these data have not been systematically analysed yet. The main objective of this systematic review was to collect evidence on the effect on suicidal behaviour of early interventions for patients in the early stages of psychotic disorders. Methods: We will carry out a systematic review of the literature according to the PRISMA criteria by searching articles in five databases (PubMed, Cochrane, PsycINFO, Scopus, EMBASE), without restriction on the publication date. The selection criteria are: articles (any type; e.g. prospective, retrospective, controlled or uncontrolled, and literature reviews) on early interventions for psychotic disorders in the early stages with data on suicide attempts, death by suicide, suicidal ideation; articles written in English or French. Exclusion criteria are: articles on suicidal behaviours in patients with psychotic disorders in the early stages, but without early intervention, and articles on early-stage psychotic disorders without data on suicidal behaviours. Discussion: If this review confirms the effectiveness on suicidal behaviours of early interventions for young patients with psychotic disorders, the development/implementation of such intervention programmes should be better promoted. [ABSTRACT FROM AUTHOR]

Background. Physical pain is a common issue in people with bipolar disorder (BD). It worsens mental health and quality of life, negatively impacts treatment response, and increases the risk of suicide. Lithium, which is prescribed in BD as a mood stabilizer, has shown promising effects on pain. Methods. This naturalistic study included 760 subjects with BD (FACE-BD cohort) divided in two groups: with and without self-reported pain (evaluated with the EQ-5D-5L questionnaire). In this sample, 176 subjects were treated with lithium salts. The objectives of the study were to determine whether patients receiving lithium reported less pain, and whether this effect was associated with the recommended mood-stabilizing blood concentration of lithium. Results. Subjects with lithium intake were less likely to report pain (odds ratio [OR] = 0.59, 95% confidence interval [CI], 0.35-0.95; p = 0.036) after controlling for sociodemographic variables, BD type, lifetime history of psychiatric disorders, suicide attempt, personality traits, current depression and anxiety levels, sleep quality, and psychomotor activity. Subjects taking lithium were even less likely to report pain when lithium concentration in blood was =0.5 mmol/l (OR = 0.45, 95% CI, 0.24-0.79; p = 0.008). Conclusions. This is the first naturalistic study to show lithium's promising effect on pain in subjects suffering from BD after controlling for many confounding variables. This analgesic effect seems independent of BD severity and comorbid conditions. Randomized controlled trials are needed to confirm the analgesic effect of lithium salts and to determine whether lithium decreases pain in other vulnerable populations. [ABSTRACT FROM AUTHOR]

Background: First episode of psychosis (FEP) is a clinical condition that usually occurs during adolescence or early adulthood and is often a sign of a future psychiatric disease. However, these symptoms are not specific, and psychosis can be caused by a physical disease in at least 5% of cases. Timely detection of these diseases, the first signs of which may appear in childhood, is of particular importance, as a curable treatment exists in most cases. However, there is no consensus in academic societies to offer recommendations for a comprehensive medical assessment to eliminate somatic causes. Methods: We conducted a systematic literature search using a two-fold research strategy to: (1) identify physical diseases that can be differentially diagnosed for psychosis; and (2) determine the paraclinical exams allowing us to exclude these pathologies. Results: We identified 85 articles describing the autoimmune, metabolic, neurologic, infectious, and genetic differential diagnoses of psychosis. Clinical presentations are described, and a complete list of laboratory and imaging features required to identify and confirm these diseases is provided. Conclusion: This systematic review shows that most differential diagnoses of psychosis should be considered in the case of a FEP and could be identified by providing a systematic checkup with a laboratory test that includes ammonemia, antinuclear and anti-NMDA antibodies, and HIV testing; brain magnetic resonance imaging and lumbar puncture should be considered according to the clinical presentation. Genetic research could be of interest to patients presenting with physical or developmental symptoms associated with psychiatric manifestations. [ABSTRACT FROM AUTHOR]

Previous studies set out profound cognitive impairments in subjects with treatment-resistant depression (TRD). However, little is known about the course of such alterations depending on levels of improvement in those patients followed longitudinally. The main objective of this study was to describe the course of cognitive impairments in responder versus non-responder TRD patients at one-year follow-up. The second aim was to evaluate the predictive aspect of cognitive impairments to treatment resistance in patients suffering from TRD. We included 131 patients from a longitudinal cohort (FACE-DR) of the French Network of Expert TRD Centers. They undertook comprehensive sociodemographic, clinical, global functioning, and neuropsychological testing (TMT, Baddeley task, verbal fluencies, WAIS-4 subtests, D2 and RLRI-16) at baseline (V0) and one-year follow-up (V1). Most patients (n = 83; 63.36%) did not respond (47 women, 49.47 ± 12.64 years old), while one-third of patients responded (n = 48, 30 women, 54.06 ± 12.03 years old). We compared the cognitive performances of participants to average theoretical performances in the general population. In addition, we compared the cognitive performances of patients between V1 and V0 and responder versus non-responder patients at V1. We observed cognitive impairments during the episode and after a therapeutic response. Overall, each of them tended to show an increase in their cognitive scores. Improvement was more prominent in responders at V1 compared to their non-responder counterparts. They experienced a more marked improvement in code, digit span, arithmetic, similarities, and D2 tasks. Patients suffering from TRD have significant cognitive impairments that persist but alleviate after therapeutic response. Cognitive remediation should be proposed after therapeutic response to improve efficiency and increase the daily functioning. [ABSTRACT FROM AUTHOR]

Background Suicide is a major public health problem and a cause of premature mortality. With a view to prevention, a great deal of research has been devoted to the determinants of suicide, focusing mostly on individual risk factors, particularly depression. In addition to causes intrinsic to the individual, the social environment has also been widely studied, particularly social isolation. This paper examines the social dimension of suicide etiology through a review of the literature on the relationship between suicide and social isolation. Methods Medline searches via PubMed and PsycINFO were conducted. The keywords were “suicid*” AND “isolation.” Results Of the 2,684 articles initially retrieved, 46 were included in the review. Conclusions Supported by proven theoretical foundations, mainly those developed by E. Durkheim and T. Joiner, a large majority of the articles included endorse the idea of a causal relationship between social isolation and suicide, and conversely, a protective effect of social support against suicide. Moreover, the association between suicide and social isolation is subject to variations related to age, gender, psychopathology, and specific circumstances. The social etiology of suicide has implications for intervention and future research. [ABSTRACT FROM AUTHOR]

Background. Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide. Methods. A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords. Results. Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings. Conclusions. AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified. [ABSTRACT FROM AUTHOR]

Introduction The early postattempt period is considered to be one of the most at-risk time windows for suicide reattempt or completion. Among the postcrisis prevention programmes developed to compensate for this risk, brief contact interventions (BCIs) have been proven to be efficient but not equally for each subpopulation of attempters. VigilanS is a region-wide programme that relies on an algorithmic system to tailor surveillance and BCI provisions to individuals discharged from the hospital after a suicide attempt. Aim VigilanS' main objective is to reduce suicide and suicide reattempt rates both at the individual level (patients included in VigilanS) and at the populational level (inhabitants of the Nord-Pas-de-Calais region). Methods and analysis At discharge, every attempter coming from a participating centre is given a crisis card with an emergency number to contact in case of distress. Patients are then systematically recontacted 6 months later. An additional 10-day call is also given if the index suicide attempt is not the first one. Depending on the clinical evaluation during the phone call, the call team may carry out proportionated crisis interventions. Personalised postcards are sent whenever patients are unreachable by phone or in distress. On the populational level, mean suicide and suicide attempt rates in Nord-Pas-de-Calais will be compared before and after the implementation of the programme. Here/there cross-sectional comparisons with a control region will test the spatial specificity of the observed fluctuations, while time-series analyses will be performed to corroborate the temporal plausibility of imputing these fluctuations to the implementation of the programme. On the individual level, patients entered in VigilanS will be prospectively compared with a matched control cohort by means of survival analyses (survival curve comparisons and Cox models). Discussion VigilanS interventional components fall under the ordinary law care regime, and the individuals' general rights as patients apply with no addendums or restrictions for their participation in the programme. The research section received authorisation from the Ethical Committee of Lille Nord-Ouest under the caption 'Study aimed at evaluating routine care' and is registered in 'Clinical Trials'. The French Ministry of Health plans to extend the experimentation to other regions and probe the relevance of this type of 'bottom-up' territorial prevention policy at the national level. [ABSTRACT FROM AUTHOR]

Background: Electronic prescribing devices with clinical decision support systems (CDSSs) hold the potential to significantly improve pharmacological treatment management.Objective: The aim of our study was to develop a novel Web- and mobile phone-based application to provide a dynamic CDSS by monitoring and analyzing practitioners' antipsychotic prescription habits and simultaneously linking these data to inpatients' symptom changes.Methods: We recruited 353 psychiatric inpatients whose symptom levels and prescribed medications were inputted into the MEmind application. We standardized all medications in the MEmind database using the Anatomical Therapeutic Chemical (ATC) classification system and the defined daily dose (DDD). For each patient, MEmind calculated an average for the daily dose prescribed for antipsychotics (using the N05A ATC code), prescribed daily dose (PDD), and the PDD to DDD ratio.Results: MEmind results found that antipsychotics were used by 61.5% (217/353) of inpatients, with the largest proportion being patients with schizophrenia spectrum disorders (33.4%, 118/353). Of the 217 patients, 137 (63.2%, 137/217) were administered pharmacological monotherapy and 80 (36.8%, 80/217) were administered polytherapy. Antipsychotics were used mostly in schizophrenia spectrum and related psychotic disorders, but they were also prescribed in other nonpsychotic diagnoses. Notably, we observed polypharmacy going against current antipsychotics guidelines.Conclusions: MEmind data indicated that antipsychotic polypharmacy and off-label use in inpatient units is commonly practiced. MEmind holds the potential to create a dynamic CDSS that provides real-time tracking of prescription practices and symptom change. Such feedback can help practitioners determine a maximally therapeutic drug treatment while avoiding unproductive overprescription and off-label use. [ABSTRACT FROM AUTHOR]

Background: Suicide is a major worldwide public health issue. Various studies showed that individual attitudes toward suicide change in a region with high suicide rate. Attitudes are one of the components of a global and complex system: social representations (SRs). Aims: In France, the Brittany region has an abnormally high death rate due to suicides. Our research focuses on the SRs of suicide in this region. The hypothesis underlying this project is that suicide SRs are different between an area with a high suicide rate and a region less affected by suicide. Method: A comparative study between the Brittany and Alsace regions, with the latter showing a statistically much lower suicide rate. The persons polled responded to a three-word free-association task around the question ‘For you, suicide is …?’ An analysis of word frequency and evocation rank was then carried out. Results: In confirmation of our hypothesis, SRs were different between Brittany and the control region. Conclusion: The study’s results open new avenues of research, specific to Brittany, in terms of the collective or individual effects of suicides, in terms of psycho-pathological conditions – essentially on depression, and in terms of training, on the stereotypes associated with suicide. [ABSTRACT FROM AUTHOR]

Background: Mobile phone text messages (short message service, SMS) are used pervasively as a form of communication. Almost 100% of the population uses text messaging worldwide and this technology is being suggested as a promising tool in psychiatry. Text messages can be sent either from a classic mobile phone or a web-based application. Reviews are needed to better understand how text messaging can be used in mental health care and other fields of medicine. Objective: The objective of the study was to review the literature regarding the use of mobile phone text messaging in mental health care. Methods: We conducted a thorough literature review of studies involving text messaging in health care management. Searches included PubMed, PsycINFO, Cochrane, Scopus, Embase and Web of Science databases on May 25, 2015. Studies reporting the use of text messaging as a tool in managing patients with mental health disorders were included. Given the heterogeneity of studies, this review was summarized using a descriptive approach. Results: From 677 initial citations, 36 studies were included in the review. Text messaging was used in a wide range of mental health situations, notably substance abuse (31%), schizophrenia (22%), and affective disorders (17%). We identified four ways in which text messages were used: reminders (14%), information (17%), supportive messages (42%), and self-monitoring procedures (42%). Applications were sometimes combined. Conclusions: We report growing interest in text messaging since 2006. Text messages have been proposed as a health care tool in a wide spectrum of psychiatric disorders including substance abuse, schizophrenia, affective disorders, and suicide prevention. Most papers described pilot studies, while some randomized clinical trials (RCTs) were also reported. Overall, a positive attitude toward text messages was reported. RCTs reported improved treatment adherence and symptom surveillance. Other positive points included an increase in appointment attendance and in satisfaction with management and health care services. Insight into message content, preventative strategies, and innovative approaches derived from the mental health field may be applicable in other medical specialties. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia is a disease associated with high burden, and improvement in care is necessary. Artificial intelligence (AI) has been used to diagnose several medical conditions as well as psychiatric disorders. However, this technology requires large amounts of data to be efficient. Social media data could be used to improve diagnostic capabilities.Objective: The objective of our study is to analyze the current capabilities of AI to use social media data as a diagnostic tool for psychotic disorders.Methods: A systematic review of the literature was conducted using several databases (PubMed, Embase, Cochrane, PsycInfo, and IEEE Xplore) using relevant keywords to search for articles published as of November 12, 2021. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify, select, and critically assess the quality of the relevant studies while minimizing bias. We critically analyzed the methodology of the studies to detect any bias and presented the results.Results: Among the 93 studies identified, 7 studies were included for analyses. The included studies presented encouraging results. Social media data could be used in several ways to care for patients with schizophrenia, including the monitoring of patients after the first episode of psychosis. We identified several limitations in the included studies, mainly lack of access to clinical diagnostic data, small sample size, and heterogeneity in study quality. We recommend using state-of-the-art natural language processing neural networks, called language models, to model social media activity. Combined with the synthetic minority oversampling technique, language models can tackle the imbalanced data set limitation, which is a necessary constraint to train unbiased classifiers. Furthermore, language models can be easily adapted to the classification task with a procedure called "fine-tuning."Conclusions: The use of social media data for the diagnosis of psychotic disorders is promising. However, most of the included studies had significant biases; we therefore could not draw conclusions about accuracy in clinical situations. Future studies need to use more accurate methodologies to obtain unbiased results. [ABSTRACT FROM AUTHOR]

Background: Suicidal behaviour and deliberate self-harm are common among adults. Research indicates that maintaining contact either via letter or postcard with at-risk adults following discharge from care services can reduce reattempt risk. Feasibility trials demonstrated that intervention through text message was also effective in preventing suicide repetition amongst suicide attempters. The aim of the current study is to investigate the effect of text message intervention versus traditional treatment on reducing the risk of suicide attempt repetition among adults after self-harm. Methods/design: The study will be a 2-year multicentric randomized controlled trial conducted by the Brest University Hospital, France. Participants will be adults discharged after self–harm, from emergency services or after a short hospitalization. Participants will be recruited over a 12-month period. The intervention is comprised of an SMS that will be sent at h48, D7, D15 and monthly. The text message enquires about the patients’ well-being and includes information regarding individual sources of help and evidence-based self help strategies. Participants will be assessed at the baseline, month 6 and 13. As primary endpoint, we will assess the number of patients who reattempt suicide in each group at 6 months. As secondary endpoints, we will assess the number of patients who reattempt suicide at 13 month, the number of suicide attempts in the intervention and control groups at 6 and 13 month, the number of death by suicide in the intervention and control groups at month 6 and 13. In both groups, suicidal ideations, will be assessed at the baseline, month 6 and 13. Medical costs and satisfaction will be assessed at month 13. Discussion: This paper describes the design and deployment of a trial SIAM; an easily reproducible intervention that aims to reduce suicide risk in adults after self-harm. It utilizes several characteristics of interventions that have shown a significant reduction in the number of suicide reattempts. We propose to assess its efficacy in reducing suicide reattempt in the suicide attempter (SA) population. [ABSTRACT FROM AUTHOR]

Aims: Evaluating French general practitioners (GPs) diagnostic knowledge and practice in the detection and treatment of early schizophrenia as well as needs and preferences with specialist services. Methods: A postal survey comprising 27 questions was conducted among 2,039 GPs from three counties of Western France. Composite scores were calculated to determine a level of diagnostic knowledge. Results: A total of 515 GPs (25.3 %) responded to the survey. The mean score to determine a level of knowledge on the most important aspects in detecting early stages of schizophrenia was 5.3 ± 2.50 [median = 6 (range 0-10)]. The mean score to determine the overall knowledge of schizophrenia was 8.1 ± 2.98 [median = 8 (range 1-16)]. The majority of surveyed GPs (76.1 %) would treat early schizophrenia pharmacologically. A majority of GPs (53.9 %) advise an insufficient duration of anti-psychotic treatment for first episode psychosis. The vast majority (90.8 %) were in favour of a specialized outpatient service. Conclusions: French GPs tend to have a deficit in diagnostic knowledge and practice in detection and treatment of early schizophrenia. It seems important to provide GPs with continuing medical education (CME) on detection and management of early schizophrenia and to set up relevant specialized outpatient services. [ABSTRACT FROM AUTHOR]

Background: Suicide attempts (SA) constitute a serious clinical problem. People who attempt suicide are at high risk of further repetition. However, no interventions have been shown to be effective in reducing repetition in this group of patients. Methods/Design: Multicentre randomized controlled trial. We examine the effectiveness of «ALGOS algorithm»: an intervention based in a decisional tree of contact type which aims at reducing the incidence of repeated suicide attempt during 6 months. This algorithm of case management comprises the two strategies of intervention that showed a significant reduction in the number of SA repeaters: systematic telephone contact (ineffective in first-attempters) and «Crisis card» (effective only in firstattempters). Participants who are lost from contact and those refusing healthcare, can then benefit from «short letters» or «postcards». Discussion: ALGOS algorithm is easily reproducible and inexpensive intervention that will supply the guidelines for assessment and management of a population sometimes in difficulties with healthcare compliance. Furthermore, it will target some of these subgroups of patients by providing specific interventions for optimizing the benefits of case management strategy. Trial Registration: The study was registered with the ClinicalTrials.gov Registry; number: NCT01123174. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients' environments using ecological momentary assessment techniques to support preventive therapeutic decision making.Objective: The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features.Methods: We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient's emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies.Results: Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this.Conclusions: We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups. [ABSTRACT FROM AUTHOR]