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Objective: To describe and compare the association between suicidality and subsequent readmission for patients hospitalised for eating disorder treatment, within two years of discharge, at two large academic medical centres in two different countries.Method: Over an 8-year study window from January 2009 to March 2017, we identified all inpatient eating disorder admissions at Weill Cornell Medicine, New York, USA (WCM) and South London and Maudsley Foundation NHS Trust, London, UK (SLaM). To establish each patient’s - suicidality profile, we applied two Natural Language Processing algorithms (NLP), independently developed at the two institutions, and detected suicidality in clinical notes documented in the first week of admission. We calculated the odds ratios for any subsequent readmission within 2 years post discharge and determined whether this was to another eating disorder unit, other psychiatric unit, a general medical hospital admission or emergency room attendance.Results: We identified 1,126 and 420 eating disorder inpatient admissions at WCM and SLaM, respectively. In the WCM cohort, evidence of above average suicidality during the first week of admission was significantly associated with an increased risk of non-eating disorder related psychiatric readmission (OR 3.48 95%CI=2.03-5.99, p valueDiscussion: Patterns of increased risk of psychiatric readmission from above average suicidality detected via NLP during inpatient eating disorder admissions differed in our two patient cohorts. However, comorbid diagnoses such as personality disorder increased the risk of any psychiatric readmission across both cohorts.

Background Self-harm in pregnancy or the year after birth (‘perinatal self-harm’) is clinically important, yet prevalence rates, temporal trends and risk factors are unclear. Methods A cohort study of 679 881 mothers (1 172 191 pregnancies) was conducted using Danish population register data-linkage. Hospital treatment for self-harm during pregnancy and the postnatal period (12 months after live delivery) were primary outcomes. Prevalence rates 1997–2015, in women with and without psychiatric history, were calculated. Cox regression was used to identify risk factors. Results Prevalence rates of self-harm were, in pregnancy, 32.2 (95% CI 28.9–35.4)/100 000 deliveries and, postnatally, 63.3 (95% CI 58.8–67.9)/100 000 deliveries. Prevalence rates of perinatal self-harm in women without a psychiatric history remained stable but declined among women with a psychiatric history. Risk factors for perinatal self-harm: younger age, non-Danish birth, prior self-harm, psychiatric history and parental psychiatric history. Additional risk factors for postnatal self-harm: multiparity and preterm birth. Of psychiatric conditions, personality disorder was most strongly associated with pregnancy self-harm (aHR 3.15, 95% CI 1.68–5.89); psychosis was most strongly associated with postnatal self-harm (aHR 6.36, 95% CI 4.30–9.41). For psychiatric disorders, aHRs were higher postnatally, particularly for psychotic and mood disorders. Conclusions Perinatal self-harm is more common in women with pre-existing psychiatric history and declined between 1997 and 2015, although not among women without pre-existing history. Our results suggest it may be a consequence of adversity and psychopathology, so preventative intervention research should consider both social and psychological determinants among women with and without psychiatric history.

Background Rates of suicide attempts and deaths are highest on Mondays and these occur more frequently in the morning or early afternoon, suggesting weekly temporal and diurnal variation in suicidal behaviour. It is unknown whether there are similar time trends on social media, of posts relevant to suicide. We aimed to determine temporal and diurnal variation in posting patterns on the Reddit forum SuicideWatch, an online community for individuals who might be at risk of, or who know someone at risk of suicide. Methods We used time series analysis to compare date and time stamps of 90,518 SuicideWatch posts from 1st December 2008 to 31st August 2015 to (i) 6,616,431 posts on the most commonly subscribed general subreddit, AskReddit and (ii) 66,934 of these AskReddit posts, which were posted by the SuicideWatch authors. Results Mondays showed the highest proportion of posts on SuicideWatch. Clear diurnal variation was observed, with a peak in the early morning (2:00–5:00 h), and a subsequent decrease to a trough in late morning/early afternoon (11:00–14:00 h). Conversely, the highest volume of posts in the control data was between 20:00–23:00 h. Conclusions Posts on SuicideWatch occurred most frequently on Mondays: the day most associated with suicide risk. The early morning peak in SuicideWatch posts precedes the time of day during which suicide attempts and deaths most commonly occur. Further research of these weekly and diurnal rhythms should help target populations with support and suicide prevention interventions when needed most.

IntroductionYoung people are the most frequent users of social media and smartphones and there has been an increasing speculation about the potential negative impacts of their use on mental health. This has coincided with a sharp increase in the levels of self-harm in young people. To date, studies researching this potential association are predominantly cross-sectional and reliant on self-report data, which precludes the ability to objectively analyse behaviour over time. This study is one of the first attempts to explore temporal patterns of real-world usage prior to self-harm, to identify whether there are usage patterns associated with an increased risk.Methods and analysisTo study the mechanisms by which social media and smartphone use underpin self-harm in a clinical sample of young people, the Social media, Smartphone use and Self-harm in Young People (3S-YP) study uses a prospective, observational study design. Up to 600 young people aged 13–25 years old from secondary mental health services will be recruited and followed for up to 6 months. Primary analysis will compare real-world data in the 7 days leading up to a participant or clinician recorded self-harm episode, to categorise patterns of problematic usage. Secondary analyses will explore potential mediating effects of anxiety, depression, sleep disturbance, loneliness and bullying.Ethics and disseminationThis study was approved by the National Research Ethics Service, London - Riverside, as well as by the Joint Research and Development Office of the Institute of Psychiatry, Psychology and Neuroscience and South London and Maudsley NHS Foundation Trust (SLaM), and the SLaM Clinical Research Interactive Search (CRIS) Oversight Committee. The findings from this study will be disseminated through peer-reviewed scientific journals, conferences, websites, social media and stakeholder engagement activities.Trial registration numberNCT04601220.

Background Duration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge about when psychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Electronic health records (EHRs) represent a useful resource for retrospective clinical studies on DUP, but the core information underlying this construct is most likely to lie in free text, meaning it is not readily available for clinical research. Natural Language Processing (NLP) is a means to addressing this problem by automatically extracting relevant information in a structured form. As a first step, it is important to identify appropriate documents, i.e., those that are likely to include the information of interest. Next, temporal information extraction methods are needed to identify time references for early psychosis symptoms. This NLP challenge requires solving three different tasks: time expression extraction, symptom extraction, and temporal “linking”. In this study, we focus on the first step, using two relevant EHR datasets. Results We applied a rule-based NLP system for time expression extraction that we had previously adapted to a corpus of mental health EHRs from patients with a diagnosis of schizophrenia (first referrals). We extended this work by applying this NLP system to a larger set of documents and patients, to identify additional texts that would be relevant for our long-term goal, and developed a new corpus from a subset of these new texts (early intervention services). Furthermore, we added normalized value annotations (“2011–05”) to the annotated time expressions (“May 2011”) in both corpora. The finalized corpora were used for further NLP development and evaluation, with promising results (normalization accuracy 71–86%). To highlight the specificities of our annotation task, we also applied the final adapted NLP system to a different temporally annotated clinical corpus. Conclusions Developing domain-specific methods is crucial to address complex NLP tasks such as symptom onset extraction and retrospective calculation of duration of a preclinical syndrome. To the best of our knowledge, this is the first clinical text resource annotated for temporal entities in the mental health domain.

ObjectiveSuicide attempts requiring hospitalization are known to be common in patients who are diagnosed with eating disorders. Attempting suicide is a major indicator for those at risk of completed suicide. Both the specific eating disorder diagnosis and the influence of psychiatric comorbidities on suicide attempts requiring hospitalization were investigated, with demographic and socioeconomic variables as confounders, over a 10‐year observation period from January 2007 to March 2017.MethodsAnonymized health‐record data from the South London and Maudsley NHS Foundation Trust (SLaM) were retrieved through the Clinical Record Interactive Search (CRIS) data resource; this is linked to national Hospital Episode Statistics (HES) data. These data include all diagnoses for inpatient admissions. Hazard ratios, with 95% confidence intervals (CIs), were calculated from cox regression analyses and the effects of a number of confounders were estimated by performing multivariable analyses.ResultsIn total, 4,895 patients were diagnosed with anorexia nervosa (AN), bulimia nervosa (BN), or eating disorder otherwise not specified (EDNOS). Of these, 331 (6.7%) had attempted suicide requiring hospitalization and 21 (0.04%) completed suicide. The eating disorder category associated with the highest risk of a suicide attempt was AN (HR: 1.43, 95%CI: 1.08–1.89, p = .01). The risk was significantly increased further if the patient had a comorbid diagnosis of personality disorder, depression, bipolar affective disorder, and substance misuse.DiscussionSuicide attempts requiring hospitalization have a high incidence rate among patients with eating disorders, and the risk is significantly increased in AN. Comorbid psychiatric illness and suicidal ideation should be carefully assessed in all eating disorder patients.

Social media usage impacts upon the mental health and wellbeing of young people, yet there is not enough evidence to determine who is affected, how and to what extent. While it has widened and strengthened communication networks for many, the dangers posed to at-risk youth are serious. Social media data offers unique insights into the minute details of a user's online life. Timely consented access to data could offer many opportunities to transform understanding of its effects on mental wellbeing in different contexts. However, limited data access by researchers is preventing such advances from being made. Our multidisciplinary authorship includes a lived experience adviser, academic and practicing psychiatrists, and academic psychology, as well as computational, statistical, and qualitative researchers. In this Perspective article, we propose a framework to support secure and confidential access to social media platform data for research to make progress toward better public mental health.

ObjectivesThe objective of this study was to determine risk factors for those diagnosed with eating disorders who report self-harm and suicidality.Design and settingThis study was a retrospective cohort study within a secondary mental health service, South London and Maudsley National Health Service Trust.ParticipantsAll diagnosed with an F50 diagnosis of eating disorder from January 2009 to September 2019 were included.Intervention and measuresElectronic health records (EHRs) for these patients were extracted and two natural language processing tools were used to determine documentation of self-harm and suicidality in their clinical notes. These tools were validated manually for attribute agreement scores within this study.ResultsThe attribute agreements for precision of positive mentions of self-harm were 0.96 and for suicidality were 0.80; this demonstrates a ‘near perfect’ and ‘strong’ agreement and highlights the reliability of the tools in identifying the EHRs reporting self-harm or suicidality. There were 7434 patients with EHRs available and diagnosed with eating disorders included in the study from the dates January 2007 to September 2019. Of these, 4591 (61.8%) had a mention of self-harm within their records and 4764 (64.0%) had a mention of suicidality; 3899 (52.4%) had mentions of both. Patients reporting either self-harm or suicidality were more likely to have a diagnosis of anorexia nervosa (AN) (self-harm, AN OR=3.44, 95% CI 1.05 to 11.3, p=0.04; suicidality, AN OR=8.20, 95% CI 2.17 to 30.1; p=0.002). They were also more likely to have a diagnosis of borderline personality disorder (p≤0.001), bipolar disorder (pConclusionA high percentage of patients (>60%) diagnosed with eating disorders report either self-harm or suicidal thoughts. Relative to other eating disorders, those diagnosed with AN were more likely to report either self-harm or suicidal thoughts. Psychiatric comorbidity, in particular borderline personality disorder and substance misuse, was also associated with an increase risk in self-harm and suicidality. Therefore, risk assessment among patients diagnosed with eating disorders is crucial.

Summary We describe the incidence of suicidality (2007–2017) in people with depression treated by secondary mental healthcare services at South London and Maudsley NHS Trust (n = 26 412). We estimated yearly incidence of ‘suicidal ideation’ and ‘high risk of suicide’ from structured and free-text fields of the Clinical Record Interactive Search system. The incidence of suicidal ideation increased from 0.6 (2007) to 1 cases (2017) per 1000 population. The incidence of high risk of suicide, based on risk forms, varied between 0.06 and 0.50 cases per 1000 adult population (2008–2017). Electronic health records provide the opportunity to examine suicidality on a large scale, but the impact of service-related changes in the use of structured risk assessment should be considered.

Risk of suicidal behaviour (SB) in schizophrenia spectrum disorders (SSD) is a major concern, particularly in early stages of the illness, when suicide accounts for a high number of premature deaths. Although some risk factors for SB in SSD are well understood, the extent to which personality traits may affect this risk remains unclear, which may have implications for prevention. We conducted a systematic review of previous studies indexed in MEDLINE, PsycINFO and Embase examining the relationship between personality traits and SB in samples of patients with SSD. Seven studies fulfilled predetermined selection criteria. Harm avoidance, passive-dependent, schizoid and schizotypal personality traits increased the risk of SB, while self-directedness, cooperativeness, excluding persistence and self-transcendence acted as protective factors. Although only seven studies were retrieved from three major databases after applying predetermined selection criteria, we found some evidence to support that personality issues may contribute to SB in patients with SSD. Personality traits may therefore become part of routine suicide risk assessment and interventions targeting these personality-related factors may contribute to prevention of SB in SSD.

Background Prevalence of self-harm in the UK was reported as 6.4% in 2014. Despite sparse evidence for effectiveness, guidelines recommend harm minimisation; a strategy in which people who self-harm are supported to do so safely. Aims To determine the prevalence, sociodemographic and clinical characteristics of those who self-harm and practise harm minimisation within a London mental health trust. Method We included electronic health records for patients treated by South London and Maudsley NHS Trust. Using an iterative search strategy, we identified patients who practise harm minimisation, then classified the approaches using a content analysis. We compared the sociodemographic characteristics with that of a control group of patients who self-harm and do not use harm minimisation. Results In total 22 736 patients reported self-harm, of these 693 (3%) had records reporting the use of harm-minimisation techniques. We coded the approaches into categories: (a) ‘substitution’ (>50% of those using harm minimisation), such as using rubber bands or using ice; (b) ‘simulation’ (9%) such as using red pens; (c) ‘defer or avoid’ (7%) such as an alternative self-injury location; (d) ‘damage limitation’ (9%) such as using antiseptic techniques; the remainder were unclassifiable (24%). The majority of people using harm minimisation described it as helpful (>90%). Those practising harm minimisation were younger, female, of White ethnicity, had previous admissions and were less likely to have self-harmed with suicidal intent. Conclusions A small minority of patients who self-harm report using harm minimisation, primarily substitution techniques, and the large majority find harm minimisation helpful. More research is required to determine the acceptability and effectiveness of harm-minimisation techniques and update national clinical guidelines.

Self-harm and suicidal ideation in children and adolescents are common and are risk factors for completed suicide. Social exclusion, which can take many forms, increases the risk of self-harm and suicidal ideation. One important marker of social exclusion in young people is school absenteeism. Whether school absenteeism is associated with these adverse outcomes, and if so to what extent, remains unclear. To determine the association between school absenteeism and both self-harm (including completed suicide) and suicidal ideation in children and adolescents, we conducted a systematic review of observational studies. We conducted meta-analysis and report a narrative synthesis where this was not possible. Meta-analysis of cross-sectional studies showed that school absenteeism was associated with an increased risk of self-harm [pooled adjusted odds ratio (aOR) 1.37, 95% confidence interval 1.20–1.57, P = 0.01] and of suicidal ideation (pooled aOR 1.20, 95% CI 1.02–1.42, P = 0.03). A small number of studies showed that school absenteeism had a longitudinal association with both adverse outcomes. Heterogeneity in the exposure and outcome variables, study design and reporting was prominent and limited the extent to which it was appropriate to pool results. School absenteeism was associated with both self-harm and suicidal ideation in young people, but this evidence was derived from a small number of cross-sectional studies. Further research into the mechanisms of this association could help to inform self-harm and suicide prevention strategies at clinical, school and population levels.

Background Suicide is a serious public health issue, accounting for 1.4% of all deaths worldwide. Current risk assessment tools are reported as performing little better than chance in predicting suicide. New methods for studying dynamic features in electronic health records (EHRs) are being increasingly explored. One avenue of research involves using sentiment analysis to examine clinicians’ subjective judgments when reporting on patients. Several recent studies have used general-purpose sentiment analysis tools to automatically identify negative and positive words within EHRs to test correlations between sentiment extracted from the texts and specific medical outcomes (eg, risk of suicide or in-hospital mortality). However, little attention has been paid to analyzing the specific words identified by general-purpose sentiment lexicons when applied to EHR corpora. Objective This study aims to quantitatively and qualitatively evaluate the coverage of six general-purpose sentiment lexicons against a corpus of EHR texts to ascertain the extent to which such lexical resources are fit for use in suicide risk assessment. Methods The data for this study were a corpus of 198,451 EHR texts made up of two subcorpora drawn from a 1:4 case-control study comparing clinical notes written over the period leading up to a suicide attempt (cases, n=2913) with those not preceding such an attempt (controls, n=14,727). We calculated word frequency distributions within each subcorpus to identify representative keywords for both the case and control subcorpora. We quantified the relative coverage of the 6 lexicons with respect to this list of representative keywords in terms of weighted precision, recall, and F score. Results The six lexicons achieved reasonable precision (0.53-0.68) but very low recall (0.04-0.36). Many of the most representative keywords in the suicide-related (case) subcorpus were not identified by any of the lexicons. The sentiment-bearing status of these keywords for this use case is thus doubtful. Conclusions Our findings indicate that these 6 sentiment lexicons are not optimal for use in suicide risk assessment. We propose a set of guidelines for the creation of more suitable lexical resources for distinguishing suicide-related from non–suicide-related EHR texts.

In the global effort to prevent death by suicide, many academic medical institutions are implementing natural language processing (NLP) approaches to detect suicidality from unstructured clinical text in electronic health records (EHRs), with the hope of targeting timely, preventative interventions to individuals most at risk of suicide. Despite the international need, the development of these NLP approaches in EHRs has been largely local and not shared across healthcare systems. In this study, we developed a process to share NLP approaches that were individually developed at King’s College London (KCL), UK and Weill Cornell Medicine (WCM), US - two academic medical centers based in different countries with vastly different healthcare systems. After a successful technical porting of the NLP approaches, our quantitative evaluation determined that independently developed NLP approaches can detect suicidality at another healthcare organization with a different EHR system, clinical documentation processes, and culture, yet do not achieve the same level of success as at the institution where the NLP algorithm was developed (KCL approach: F1-score 0.85 vs. 0.68, WCM approach: F1-score 0.87 vs. 0.72). Shared use of these NLP approaches is a critical step forward towards improving data-driven algorithms for early suicide risk identification and timely prevention.

Suicide is a major public health problem worldwide and continues to be one of the main causes of death. Implementing surveillance strategies for suicidal thoughts and behaviours would make it possible to identify individuals at high risk of ending their lives by suicide. While a universal screening would be controversial, the increasing use of the 9-item version of the Patient Health Questionnaire (PHQ-9) in different healthcare settings, such as primary care or hospital emergency departments, offers an opportunity for testing its performance for suicide surveillance. Beyond being a screening of depression, the PHQ-9 has shown merit as a marker of suicidal thinking, thoughts of self-harm, and suicide. Implementing systematic surveillance strategies for suicide in different healthcare settings including data from the PHQ-9 might be an effective way to improve case detection. This could help to enhance the identification of highest risk population groups and, consequently, to avoid potentially preventable suicides.

Background Self-harm occurring within pregnancy and the postnatal year (“perinatal self-harm”) is a clinically important yet under-researched topic. Current research likely under-estimates prevalence due to methodological limitations. Electronic healthcare records (EHRs) provide a source of clinically rich data on perinatal self-harm. Aims (1) To create a Natural Language Processing (NLP) tool that can, with acceptable precision and recall, identify mentions of acts of perinatal self-harm within EHRs. (2) To use this tool to identify service-users who have self-harmed perinatally, based on their EHRs. Methods We used the Clinical Record Interactive Search system to extract de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS Foundation Trust. We developed a tool that applied several layers of linguistic processing based on the spaCy NLP library for Python. We evaluated mention-level performance in the following domains: span, status, temporality and polarity. Evaluation was done against a manually coded reference standard. Mention-level performance was reported as precision, recall, F-score and Cohen’s kappa for each domain. Performance was also assessed at ‘service-user’ level and explored whether a heuristic rule improved this. We report per-class statistics for service-user performance, as well as likelihood ratios and post-test probabilities. Results Mention-level performance: micro-averaged F-score, precision and recall for span, polarity and temporality >0.8. Kappa for status 0.68, temporality 0.62, polarity 0.91. Service-user level performance with heuristic: F-score, precision, recall of minority class 0.69, macro-averaged F-score 0.81, positive LR 9.4 (4.8–19), post-test probability 69.0% (53–82%). Considering the task difficulty, the tool performs well, although temporality was the attribute with the lowest level of annotator agreement. Conclusions It is feasible to develop an NLP tool that identifies, with acceptable validity, mentions of perinatal self-harm within EHRs, although with limitations regarding temporality. Using a heuristic rule, it can also function at a service-user-level.

The lockdown and social distancing policy imposed due to the COVID-19 pandemic has had a substantial impact on both mental health service delivery, and the ways in which people are accessing these services. Previous reports from the South London and Maudsley NHS Trust (SLaM; a large mental health service provider for around 1.2m residents in South London) have highlighted increased use of virtual contacts by mental health teams, with dropping numbers of face-to-face contacts over the first wave of the pandemic. There has been concern that the impact of the COVID-19 pandemic would lead to higher mental health emergencies, particularly instances of self-harm. However, with people advised to stay at home during the ‘first wave’ lockdown, it is as yet unclear whether this impacted mental health service presentations. Taking advantage of SLaM’s Clinical Records Interactive Search (CRIS) data resource with daily updates of information from its electronic mental health records, this paper describes overall presentations to Emergency Department (ED) mental health liaison teams, and those with self-harm. The paper focussed on three periods: i) a pre-lockdown period 1st February to 15th March, ii) a lockdown period 16th March to 10th May and iii) a post-lockdown period 11th May to 28th June. In summary, all attendances to EDs for mental health support decreased during the lockdown period, including those with self-harm. All types of self-harm decreased during lockdown, with self-poisoning remaining the most common. Attendances to EDs for mental health support increased post-lockdown, although were only just approaching pre-lockdown levels by the end of June 2020.

Suicide is a serious public health issue worldwide, yet current clinical methods for assessing a person's risk of taking their own life remain unreliable and new methods for assessing suicide risk are being explored. The widespread adoption of electronic health records (EHRs) has opened up new possibilities for epidemiological studies of suicide and related behaviour amongst those receiving healthcare. These types of records capture valuable information entered by healthcare practitioners at the point of care. However, much recent work has relied heavily on the structured data of EHRs, whilst much of the important information about a patient's care pathway is recorded in the unstructured text of clinical notes. Accessing and structuring text data for use in clinical research, and particularly for suicide and self-harm research, is a significant challenge that is increasingly being addressed using methods from the fields of natural language processing (NLP) and machine learning (ML). In this review, we provide an overview of the range of suicide-related studies that have been carried out using the Clinical Records Interactive Search (CRIS): a database for epidemiological and clinical research that contains de-identified EHRs from the South London and Maudsley NHS Foundation Trust. We highlight the variety of clinical research questions, cohorts and techniques that have been explored for suicide and related behaviour research using CRIS, including the development of NLP and ML approaches. We demonstrate how EHR data provides comprehensive material to study prevalence of suicide and self-harm in clinical populations. Structured data alone is insufficient and NLP methods are needed to more accurately identify relevant information from EHR data. We also show how the text in clinical notes provide signals for ML approaches to suicide risk assessment. We envision increased progress in the decades to come, particularly in externally validating findings across multiple sites and countries, both in terms of clinical evidence and in terms of NLP and machine learning method transferability.

Aims1.To generate a Natural Language Processing (NLP) application that can identify mentions of perinatal self-harm among electronic healthcare records (EHRs)2.To use this application to estimate the prevalence of perinatal self-harm within a data-linkage cohort of women accessing secondary mental healthcare during the perinatal period.MethodData source: the Clinical Record Interactive Search system. This is a database of de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS Foundation Trust (SLaM). CRIS has pre-existing ethical approval via the Oxfordshire Research Ethics Committee C (ref 18/SC/0372) and this project was approved by the CRIS Oversight Committee (16-069). After developing a list of synonyms for self-harm and piloting coding rules, a gold standard dataset of EHRs was manually coded using Extensible Human Oracle Suite of Tools (eHOST) software. An NLP application to detect perinatal self-harm was then developed using several layers of linguistic processing based on the spaCy NLP library for Python. Evaluation of mention-level performance was done according to the attributes of mentions the application was designed to identify (span, status, temporality and polarity), by comparing application performance against the gold standard dataset. Performance was described as precision, recall, F-score and Cohen's kappa. Most service-users had more than one EHR in their period of perinatal service use. Performance was therefore also measured at “service-user level” with additional performance metrics of likelihood ratios and post-test probabilities. Linkage with the Hospital Episode Statistics datacase allowed creation of a cohort of women who accessed SLaM during the perinatal period. By deploying the application on the EHRs of the women in the cohort, we were able to estimate the prevalence of perinatal self-harm.ResultMention-level performance: micro-averaged F-score, precision and recall for span, polarity and temporality all >0.8. Kappa for status 0.68, temporality 0.62, polarity 0.91. Service-user level performance: F-score, precision, recall all 0.69, overall F-score 0.81, positive likelihood ratio 9.4 (4.8–19), post-test probability 68.9% (95%CI 53–82).Cohort prevalence of self-harm in pregnancy was 15.3% (95% CI 14.3–16.3); self-harm in the postnatal year was 19.7% (95% CI 18.6–20.8). Only a very small proportion of women self-harmed in both pregnancy and the postnatal year (3.9%, 95% CI 3.3–4.4).ConclusionNLP can be used to identify perinatal self-harm within EHRs. The hardest attribute to classify was temporality. This is in line with the wider literature indicating temporality as a notoriously difficult problem in NLP. As a result, the application probably over-estimates prevalence, to a degree. However, overall performance, given the difficulty of the task, is good.Bearing in mind the limitations, our findings suggest that self-harm is likely to be relatively common in women accessing secondary mental healthcare during the perinatal period.Funding: KA is funded by a National Institute for Health Research Doctoral Research Fellowship (NIHR-DRF-2016-09-042). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. RD is funded by a Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences which also party funds AB. AB's work was also part supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities, as well as the Maudsley Charity.Acknowledgements: Professor Louise M Howard, who originally suggested using NLP to identify perinatal self-harm in EHRs. Professor Howard is the primary supervisor of KA's Fellowship.

BACKGROUND Suicide is a serious public health issue, accounting for 1.4% of all deaths worldwide. Current risk assessment tools are reported as performing little better than chance in predicting suicide. New methods for studying dynamic features in electronic health records (EHRs) are being increasingly explored. One avenue of research involves using sentiment analysis to examine clinicians’ subjective judgments when reporting on patients. Several recent studies have used general-purpose sentiment analysis tools to automatically identify negative and positive words within EHRs to test correlations between sentiment extracted from the texts and specific medical outcomes (eg, risk of suicide or in-hospital mortality). However, little attention has been paid to analyzing the specific words identified by general-purpose sentiment lexicons when applied to EHR corpora. OBJECTIVE This study aims to quantitatively and qualitatively evaluate the coverage of six general-purpose sentiment lexicons against a corpus of EHR texts to ascertain the extent to which such lexical resources are fit for use in suicide risk assessment. METHODS The data for this study were a corpus of 198,451 EHR texts made up of two subcorpora drawn from a 1:4 case-control study comparing clinical notes written over the period leading up to a suicide attempt (cases, n=2913) with those not preceding such an attempt (controls, n=14,727). We calculated word frequency distributions within each subcorpus to identify representative keywords for both the case and control subcorpora. We quantified the relative coverage of the 6 lexicons with respect to this list of representative keywords in terms of weighted precision, recall, and F score. RESULTS The six lexicons achieved reasonable precision (0.53-0.68) but very low recall (0.04-0.36). Many of the most representative keywords in the suicide-related (case) subcorpus were not identified by any of the lexicons. The sentiment-bearing status of these keywords for this use case is thus doubtful. CONCLUSIONS Our findings indicate that these 6 sentiment lexicons are not optimal for use in suicide risk assessment. We propose a set of guidelines for the creation of more suitable lexical resources for distinguishing suicide-related from non–suicide-related EHR texts.

Chronic fatigue syndrome (CFS) is a long-term illness with a wide range of symptoms and condition trajectories. To improve the understanding of these, automated analysis of large amounts of patient data holds promise. Routinely documented assessments are useful for large-scale analysis, however relevant information is mainly in free text. As a first step to extract symptom and condition trajectories, natural language processing (NLP) methods are useful to identify important textual content and relevant information. In this paper, we propose an agnostic NLP method of extracting segments of patients' clinical histories in CFS assessments. Moreover, we present initial results on the advantage of using these segments to quantify and analyse the presence of certain clinically relevant concepts.

Introduction: Caregivers for people with dementia face a number of challenges such as changing family relationships, social isolation, or financial difficulties. Internet usage and social media are increasingly being recognised as resources to increase support and general public health. Objective: Using automated analysis, the aim of this study was to explore (i) the age and sex of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) which subreddits authors are posting to, (iv) the types of messages posted, and (v) the content of these posts. Methods: We analysed Reddit posts concerning dementia diagnoses and used a previously developed text analysis pipeline to determine attributes of the posts and their authors. The posts were further examined through manual annotation of the diagnosis provided and the person affected. Lastly, we investigated the communities posters engage with and assessed the contents of the posts with an automated topic gathering/clustering technique. Results: Five hundred and thirty-five Reddit posts were identified as relevant and further processed. The majority of posters in our dataset are females and predominantly close relatives, such as parents and grandparents, are mentioned. The communities frequented and topics gathered reflect not only the person’s diagnosis but also potential outcomes, for example hardships experienced by the caregiver or the requirement for legal support. Conclusions: This work demonstrates the value of social media data as a resource for in-depth examination of caregivers’ experience after a dementia diagnosis. It is important to study groups actively posting online, both in topic-specific and general communities, as they are most likely to benefit from novel internet-based support systems or interventions.

Background: Despite the strong links between mental disorder, self-harm and suicide, there is little prospective data on risk factors for later suicide in women who experience perinatal mental disorders. We aimed to examine later suicide in a cohort of women in contact with a mental healthcare provider perinatally and whether self-harm increased suicide risk. Methods: Via data-linkage, de-identified service-user electronic healthcare records, national hospital episode statistics and mortality data generated a clinical cohort of women who gave birth in hospital and were in contact with a mental healthcare provider in London, UK, perinatally. Using Natural Language Processing and structured field extraction, we identified clinical, socio-demographic characteristics, self-harm, and suicide. Outcomes: Among 5204 women, crude rate of suicide 9·69/100 000 person-years (95% CI 3·64 - 25·82) within one year of delivery; 27·56 (15·26 - 49·77) within two, and 55·12 (36·29 - 83·71) during total follow up time (39,912·9 person-years). Characteristics of women who did and did not die by suicide were similar apart from illness severity indicators, which were more common in women who died by suicide. In a Cox regression model, lifetime self-harm was not associated with later suicide. Suicides occurred most frequently in the second year after delivery, most commonly by violent means. Suicides beyond two years were most commonly by poisoning. Interpretation: This study provides support for the extension of perinatal mental healthcare to two years post-delivery, particularly for women with severe mental disorders. Funding : The National Institute for Health Research, King's College London Biomedical Research Council, Health Foundation, Academy of Medical Sciences. Declaration of Interests: None. Ethics Approval Statement: CRIS has pre-existing ethical approval via the Oxfordshire Research Ethics Committee C (ref 18/SC/0372). The use of HES data is included in the CRIS REC approval and the BRC also have approval through the Health Research Authority’s support under Section 251 of the NHS Act 2006 (Ref: ECC 3- 04(f)/2011). Linkage between CRIS and ONS Mortality data is approved via the Health Research Authority’s support under Section 251 of the NHS Act 2006. Individual CRIS projects require ethical approval from the CRIS Oversight Committee (approval 16-069), which is chaired by a service-user and member of the SLAM BRC Stakeholder Participation theme.