Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach

Hettige, Nuwan C.; Nguyen, Thai Binh; Chen, Yuan; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani‐Fatemi, Ali; Voineskos, Aristotle N.; Chakravarty, M. Mallar; Luca, Vincenzo De

doi:10.1016/j.genhosppsych.2017.03.001

Cited by 48 publications

(38 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning methods based on predictive (classification) models using RF have recently been widely applied in many diagnostic, prognostic and therapeutic studies (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52). Machine learning methods based on predictive (classification) models using RF have recently been widely applied in many diagnostic, prognostic and therapeutic studies (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52).…”

Section: Discussionmentioning

confidence: 99%

“…Our study provides evidence for the clinical benefits of quantitative immunophenotyping by a rational and effective marker panel followed by the use of a predictive model (diagnostic classifier), minimizing the subjectivity of commonly used expert-based assessment. Machine learning methods based on predictive (classification) models using RF have recently been widely applied in many diagnostic, prognostic and therapeutic studies (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52). Hereby we also showed its utility for the evaluation of flow cytometry data.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative assessment of informative immunophenotypic markers increases the diagnostic value of immunophenotyping in mature CD5‐positive B‐cell neoplasms

Starostka

Kriegová

Kudělka

et al. 2018

Cytometry Part B Clinical

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantitative assessment of informative immunophenotypic markers increases the diagnostic value of immunophenotyping in mature CD5‐positive B‐cell neoplasms

Starostka

Kriegová

Kudělka

et al. 2018

Cytometry Part B Clinical

View full text Add to dashboard Cite

show abstract

“…Beyond patients with low mood, prediction models have successfully classified suicide risk in patients with other psychiatric (Gradus et al, 2017; Hettige et al, 2017) and medical (Kalinin and Polyanskiy, 2005) diagnoses, and even among seemingly healthy demographics such as students (Mortier et al, 2018), prisoners (Bonner and Rich, 1990) and soldiers (Kessler et al, 2015, 2017). Soldiers, in particular, have been considered a high-risk group with an identified need for targeted prediction models.…”

Section: Role Of Ai In Suicide Risk Predictionmentioning

confidence: 99%

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

Fonseka

Bhat

Kennedy

2019

Aust N Z J Psychiatry

View full text Add to dashboard Cite

Objective: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management. Methods: This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance. Results: At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports. Conclusion: Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

show abstract

“…Since then, very few machine learning suicide prediction studies have been published, with the majority of suicide prediction studies using machine learning being published in the past decade. Relative to prediction studies, applications of machine learning to cross‐sectional data are more common (e.g., Fernandes et al, ; Hettige et al, ). Although this pattern echoes the broader suicide literature (Franklin et al, ), longitudinal study designs are critical to inform risk (Kraemer et al, ).…”

Section: Overview Of the Existing Literaturementioning

confidence: 99%

Machine learning in suicide science: Applications and ethics

Linthicum

Schafer

Ribeiro

2019

Behavioral Sci & The Law

106

View full text Add to dashboard Cite

For decades, our ability to predict suicide has remained at near‐chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.

show abstract

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach

Cited by 48 publications

References 36 publications

Quantitative assessment of informative immunophenotypic markers increases the diagnostic value of immunophenotyping in mature CD5‐positive B‐cell neoplasms

Quantitative assessment of informative immunophenotypic markers increases the diagnostic value of immunophenotyping in mature CD5‐positive B‐cell neoplasms

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

Machine learning in suicide science: Applications and ethics

Contact Info

Product

Resources

About