Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.

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.

Machine Learning and Natural Language Processing in Mental Health: Systematic Review.

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.

[Preventing suicide in general practice]. / Prévenir le suicide en médecine générale.

With one of the highest rates in Europe, suicide is a major public health problem in France. General practitioners are the backbone of the health system. They play a major role in suicide prevention. Mental health accounts for a large proportion of primary care practitioners' patients, the majority of which are for depressive and anxiety disorders. However, patients prefer to go to their general practitioner when they first encounter psychological difficulties. They are more likely to consult a mental health professional based on the advice of their general practitioner, hence the importance of linking primary and more specialised care.

Clinicians' Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App.

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.

The Use of Virtual Reality in Patients with Eating Disorders: Systematic Review.

BACKGROUND: Patients with eating disorders are characterized by pathological eating habits and a tendency to overestimate their weight and body shape. Virtual reality shows promise for the evaluation and management of patients with eating disorders. This technology, when accepted by this population, allows immersion in virtual environments, assessment, and therapeutic approaches, by exposing users to high-calorie foods or changes in body shape. OBJECTIVE: To better understand the value of virtual reality, we conducted a review of the literature, including clinical studies proposing the use of virtual reality for the evaluation and management of patients with eating disorders. METHODS: We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and Web of Science up to April 2017. We created the list of keywords based on two domains: virtual reality and eating disorders. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify, select, and critically appraise relevant research while minimizing bias. RESULTS: The initial database searches identified 311 articles, 149 of which we removed as duplicates. We analyzed the resulting set of 26 unique studies that met the inclusion criteria. Of these, 8 studies were randomized controlled trials, 13 were nonrandomized studies, and 5 were clinical trials with only 1 participant. Most articles focused on clinical populations (19/26, 73%), with the remainder reporting case-control studies (7/26, 27%). Most of the studies used visual immersive equipment (16/26, 62%) with a head-mounted display (15/16, 94%). Two main areas of interest emerged from these studies: virtual work on patients' body image (7/26, 27%) and exposure to virtual food stimuli (10/26, 38%). CONCLUSIONS: We conducted a broad analysis of studies on the use of virtual reality in patients with eating disorders. This review of the literature showed that virtual reality is an acceptable and promising therapeutic tool for patients with eating disorders.

Effect of early intervention for early-stage psychotic disorders on suicidal behaviours - a systematic review protocol.

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. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021237833.

Psychosis Caused by a Somatic Condition: How to Make the Diagnosis? A Systematic Literature Review.

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.

Social isolation and suicide risk: Literature review and perspectives.

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.

Clinical Applications of Mobile Health Wearable-Based Sleep Monitoring: Systematic Review.

BACKGROUND: Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health. OBJECTIVE: The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic. METHODS: We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults. RESULTS: The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable-based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography. CONCLUSIONS: This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring.

Impact of a Terrorist Attack on the Mental Health of Directly Exposed French Adolescents: Study Protocol for the First Step of the AVAL Cohort Study.

Background: Several terrorist attacks have recently taken place in France and Europe. Various studies have shown a high prevalence of Post-Traumatic Stress Disorder (PTSD) and other psychiatric disorders among the victims of these attacks. Nevertheless, research in this field is scarce and no cohort study has been conducted yet to evaluate the impact of a terrorist attack on teenagers directly exposed to this type of events. Therefore, we decided to work on the AVAL (Adolescents Victimes de l'Attentat de Londres) cohort study in order to measure the psycho-traumatic impact of this attack and to describe these adolescents' health care pathways. Material and method: The 53 students of a French high school who were directly exposed (criterion A1 of PTSD in DSM-5) to the terrorist attack perpetrated in London on March 22, 2017 constitute the target population of this monocentric cross-sectional observational study. We decided not to include the three students who were physically wounded and, therefore, didn't have the same sensorial exposition. The primary endpoint will be the prevalence of PTSD 12 to 15 months after the attack, measured by the PCL-5 (Post-traumatic stress disorder Check-List for DSM-5) global severity score: the diagnosis of PTSD will be retained when the score is > 32. We will also use an extensive battery of clinical tests to assess the prevalence of anxiety disorders, mood disorders, sleep disorders, addictions, suicide risk, and alterations in social, family, and school functioning 12 to 15 months after the attack. We will also describe these adolescents' health care pathways since the attack and collect data from the clinical evaluation performed during the initial intervention of the medico-psychological emergency cell within 10 days after the attack. Discussion: The findings of this study are intended to provide epidemiological data about the psycho-traumatic impact of a terrorist attack on the mental health of directly exposed adolescents and to describe these adolescents' health care pathways, thus contributing to improve the immediate, post-immediate, and delayed response strategies after a major psycho-traumatic event involving adolescents (and in particular after terrorist attacks), as well as the identification and psychiatric care of the young survivors requiring specialized care. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT03493243. Ethics and Dissemination: The regional ethics committee (Comité de Protection des Personnes Ouest IV-Nantes) approved the study protocol (Reference 10/18_3). All participants (and their legal guardians, for minors) must sign the informed consent to participate. The protocol was presented at the French congress of psychiatry in Nantes (France) in November 2018. After study completion, the results will be published and detailed in Marion Grenon's MD thesis in psychiatry.

Toward mHealth Brief Contact Interventions in Suicide Prevention: Case Series From the Suicide Intervention Assisted by Messages (SIAM) Randomized Controlled Trial.

BACKGROUND: Research indicates that maintaining contact either via letter or postcard with at-risk adults following discharge from care services after a suicide attempt (SA) can reduce reattempt risk. Pilot studies have demonstrated that interventions using mobile health (mHealth) technologies are feasible in a suicide prevention setting. OBJECTIVE: The aim of this study was to report three cases of patients recruited in the Suicide Intervention Assisted by Messages (SIAM) study to describe how a mobile intervention may influence follow-up. METHODS: SIAM is a 2-year, multicenter randomized controlled trial conducted by the Brest University Hospital, France. Participants in the intervention group receive SIAM text messages 48 hours after discharge, then at day 8 and day 15, and months 1, 2, 3, 4, 5, and 6. The study includes participants aged 18 years or older, who have attended a participating hospital for an SA, and have been discharged from the emergency department (ED) or a psychiatric unit (PU) for a stay of less than 7 days. Eligible participants are randomized between the SIAM intervention messages and a control group. In this study, we present three cases from the ongoing SIAM study that demonstrate the capability of a mobile-based brief contact intervention for triggering patient-initiated contact with a crisis support team at various time points throughout the mobile-based follow-up period. RESULTS: Out of the 244 patients recruited in the SIAM randomized controlled trial, three cases were selected to illustrate the impact of mHealth on suicide risk management. Participants initiated contact with the emergency crisis support service after receiving text messages up to 6 months following discharge from the hospital. Contact was initiated immediately following receipt of a text message or up to 6 days following a message. CONCLUSIONS: This text message-based brief contact intervention has demonstrated the potential to reconnect suicidal individuals with crisis support services while they are experiencing suicidal ideation as well as in a period after receiving messages. As follow-up phone calls over an extended period of time may not be feasible, this intervention has the potential to offer simple technological support for individuals following discharge from the ED. TRIAL REGISTRATION: ClinicalTrials.gov NCT02106949; https://clinicaltrials.gov/ct2/show/NCT02106949 (Archived by WebCite at http://www.webcitation.org/6wMtAFL49).