The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

High-performance medicine: the convergence of human and artificial intelligence

Over the past decade, the interpersonal theory of suicide has contributed to substantial advances in the scientific and clinical understanding of suicide and related conditions. The interpersonal theory of suicide posits that suicidal desire emerges when individuals experience intractable feelings of perceived burdensomeness and thwarted belongingness and near-lethal or lethal suicidal behavior occurs in the presence of suicidal desire and capability for suicide. A growing number of studies have tested these posited pathways in various samples; however, these findings have yet to be evaluated meta-analytically. This paper aimed to (a) conduct a systematic review of the unpublished and published, peer-reviewed literature examining the relationship between interpersonal theory constructs and suicidal thoughts and behaviors, (b) conduct meta-analyses testing the interpersonal theory hypotheses, and (c) evaluate the influence of various moderators on these relationships. Four electronic bibliographic databases were searched through the end of March, 2016: PubMed, Medline, PsycINFO, and Web of Science. Hypothesis-driven meta-analyses using random effects models were conducted using 122 distinct unpublished and published samples. Findings supported the interpersonal theory: the interaction between thwarted belongingness and perceived burdensomeness was significantly associated with suicidal ideation; and the interaction between thwarted belongingness, perceived burdensomeness, and capability for suicide was significantly related to a greater number of prior suicide attempts. However, effect sizes for these interactions were modest. Alternative configurations of theory variables were similarly useful for predicting suicide risk as theory-consistent pathways. We conclude with limitations and recommendations for the interpersonal theory as a framework for understanding the suicidal spectrum. (PsycINFO Database Record

The interpersonal theory of suicide: A systematic review and meta-analysis of a decade of cross-national research.

Suicide is a major public health concern accounting for 800 000 deaths globally each year. Although there have been many advances in understanding suicide risk in recent decades, our ability to pre...

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2017.0268

The integrated motivational-volitional model of suicidal behaviour.

Two studies examined 2 important but previously unanswered questions about the experience of suicidal ideation: (a) How does suicidal ideation vary over short periods of time?, and (b) To what degree do risk factors for suicidal ideation vary over short periods and are such changes associated with changes in suicidal ideation? Participants in Study 1 were 54 adults who had attempted suicide in the previous year and completed 28 days of ecological momentary assessment (EMA; average of 2.51 assessments per day; 2,891 unique assessments). Participants in Study 2 were 36 adult psychiatric inpatients admitted for suicide risk who completed EMA throughout their time in the hospital (average stay of 10.32 days; average 2.48 assessments per day; 649 unique assessments). These studies revealed 2 key findings: (a) For nearly all participants, suicidal ideation varied dramatically over the course of most days: more than 1-quarter (Study 1 = 29%; Study 2 = 28%) of all ratings of suicidal ideation were a standard deviation above or below the previous response from a few hours earlier and nearly all (Study 1 = 94.1%; Study 2 = 100%) participants had at least 1 instance of intensity of suicidal ideation changing by a standard deviation or more from 1 response to the next. (b) Across both studies, well-known risk factors for suicidal ideation such as hopelessness, burdensomeness, and loneliness also varied considerably over just a few hours and correlated with suicidal ideation, but were limited in predicting short-term change in suicidal ideation. These studies represent the most fine-grained examination of suicidal ideation ever conducted. The results advance the understanding of how suicidal ideation changes over short periods and provide a novel method of improving the short-term prediction of suicidal ideation. (PsycINFO Database Record

Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies.

Although recent years have seen large decreases in the overall global rate of suicide fatalities, this trend is not reflected everywhere. Suicide and suicidal behaviour continue to present key challenges for public policy and health services, with increasing suicide deaths in some countries such as the USA. The development of suicide risk is complex, involving contributions from biological (including genetics), psychological (such as certain personality traits), clinical (such as comorbid psychiatric illness), social and environmental factors. The involvement of multiple risk factors in conveying risk of suicide means that determining an individual’s risk of suicide is challenging. Improving risk assessment, for example, by using computer testing and genetic screening, is an area of ongoing research. Prevention is key to reduce the number of suicide deaths and prevention efforts include universal, selective and indicated interventions, although these interventions are often delivered in combination. These interventions, combined with psychological (such as cognitive behavioural therapy, caring contacts and safety planning) and pharmacological treatments (for example, clozapine and ketamine) along with coordinated social and public health initiatives, should continue to improve the management of individuals who are suicidal and decrease suicide-associated morbidity. Suicide and suicidal behaviour continue to present key challenges for public policy and health services. This Primer discusses the global burden of suicide and suicidal behaviours, and provides an overview of our current understanding of the mechanisms of suicide, including risk factors for suicidal ideation and the transition from ideation to suicide attempt.

/pdf/suicide-and-suicide-risk-2mwsvcppvb.pdf

Suicide and suicide risk

Suicidal thoughts and behaviors (STBs) are major public health problems that have not declined appreciably in several decades. One of the first steps to improving the prevention and treatment of STBs is to establish risk factors (i.e., longitudinal predictors). To provide a summary of current knowledge about risk factors, we conducted a meta-analysis of studies that have attempted to longitudinally predict a specific STB-related outcome. This included 365 studies (3,428 total risk factor effect sizes) from the past 50 years. The present random-effects meta-analysis produced several unexpected findings: across odds ratio, hazard ratio, and diagnostic accuracy analyses, prediction was only slightly better than chance for all outcomes; no broad category or subcategory accurately predicted far above chance levels; predictive ability has not improved across 50 years of research; studies rarely examined the combined effect of multiple risk factors; risk factors have been homogenous over time, with 5 broad categories accounting for nearly 80% of all risk factor tests; and the average study was nearly 10 years long, but longer studies did not produce better prediction. The homogeneity of existing research means that the present meta-analysis could only speak to STB risk factor associations within very narrow methodological limits-limits that have not allowed for tests that approximate most STB theories. The present meta-analysis accordingly highlights several fundamental changes needed in future studies. In particular, these findings suggest the need for a shift in focus from risk factors to machine learning-based risk algorithms. (PsycINFO Database Record

Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research.

Background Certain demographic factors have long been cited to confer risk or protection for suicidal thoughts and behaviors. However, many studies have found weak or non-significant effects. Determining the effect strength and clinical utility of demographics as predictors is crucial for suicide risk assessment and theory development. As such, we conducted a meta-analysis to determine the effect strength and clinical utility of demographics as predictors. Methods We searched PsycInfo, PubMed, and GoogleScholar for studies published before January 1st, 2015. Inclusion criteria required that studies use at least one demographic factor to longitudinally predict suicide ideation, attempt, or death. The initial search yielded 2,541 studies, 159 of which were eligible. A total of 752 unique statistical tests were included in analysis. Results Suicide death was the most commonly studied outcome, followed by attempt and ideation. The average follow-up length was 9.4 years. The overall effects of demographic factors studied in the field as risk factors were significant but weak, and that of demographic factors studied as protective factors were non-significant. Adjusting for publication bias further reduced effect estimates. No specific demographic factors appeared to be strong predictors. The effects were consistent across multiple moderators. Conclusions At least within the narrow methodological constraints of the existing literature, demographic factors were statistically significant risk factors, but not protective factors. Even as risk factors, demographics offer very little improvement in predictive accuracy. Future studies that go beyond the limitations of the existing literature are needed to further understand the effects of demographics.

/pdf/demographics-as-predictors-of-suicidal-thoughts-and-17etdfl8n1.pdf

Katherine M. Musacchio

Papers

Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research.

Demographics as predictors of suicidal thoughts and behaviors: A meta-analysis.