Integrating Predictive Modeling Into Mental Health Care: An Example in Suicide Prevention

Reger, Greg M.; McClure, Mary Lou; Ruskin, David; Carter, Sarah P.; Reger, Mark A.

doi:10.1176/appi.ps.201800242

Cited by 37 publications

(32 citation statements)

References 9 publications

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“…It is important to collect good quality data that can be used to model recovery, reduction in suicide, and prediction of suicide. This will require an integrated approach bringing people from many specialties together (Adamou, Antoniou, Greasidou, et al, ; Reger, McClure, Ruskin, Carter, & Reger, ).…”

Section: Summary Of Findingsmentioning

confidence: 99%

Preventing suicide, promoting resilience: Is this achievable from a global perspective?

Ivbijaro

Kolkiewicz

Goldberg

et al. 2019

Asia-Pacific Psychiatry

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Suicide continues to be a major health concern globally despite many initiatives to identify risk factors and methods for suicide prevention. We have carried out a detailed narrative review of the literature from 2016 to 2019 using the headings of Personal resilience (P1), People (P2), Places (P3), Prevention (P4), Promoting collaboration (P5), and Promoting research (P6) in order to support an integrated approach to suicide prevention and the promotion of personal and population resilience. We have made 10 key recommendations on how this can be moved forward.

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Section: Summary Of Findingsmentioning

confidence: 99%

Preventing suicide, promoting resilience: Is this achievable from a global perspective?

Ivbijaro

Kolkiewicz

Goldberg

et al. 2019

Asia-Pacific Psychiatry

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“…Using this algorithm or something similar may help paraprofessionals better identify those at risk and reach them with culturally appropriate care before they engage in serious suicidal behaviors. Several large health care settings have started to use these approaches linked to their electronic medical records data (EHR) (Kessler, Hwang, et al, ; Kessler, Stein, et al, ; Reger, McClure, Ruskin, Carter, & Reger, ; Walsh et al, , ), suggesting this is a potentially scalable approach throughout Indian Health Service (IHS) and tribally run hospitals and clinics. More research is needed to validate this model in other tribal settings, continuously improve this model with data from participating tribes, and/or generate new models with EHR data from other tribal settings, involving key stakeholders in every stage of development, implementation, and analysis (Jeffery, ).…”

Section: Discussionmentioning

confidence: 99%

Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities

Haroz

Walsh

Goklish

et al. 2019

Suicide & Life Threat Behav

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Objective Suicide prevention is a major priority in Native American communities. We used machine learning with community‐based suicide surveillance data to better identify those most at risk. Method This study leverages data from the Celebrating Life program operated by the White Mountain Apache Tribe in Arizona and in partnership with Johns Hopkins University. We examined N = 2,390 individuals with a validated suicide‐related event between 2006 and 2017. Predictors included 73 variables (e.g., demographics, educational history, past mental health, and substance use). The outcome was suicide attempt 6, 12, and 24 months after an initial event. We tested four algorithmic approaches using cross‐validation. Results Area under the curves ranged from AUC = 0.81 (95% CI ± 0.08) for the decision tree classifiers to AUC = 0.87 (95% CI ± 0.04) for the ridge regression, results that were considerably higher than a past suicide attempt (AUC = 0.57; 95% CI ± 0.08). Selecting a cutoff value based on risk concentration plots yielded 0.88 sensitivity, 0.72 specificity, and a positive predictive value of 0.12 for detecting an attempt 24 months postindex event. Conclusion These models substantially improved our ability to determine who was most at risk in this community. Further work is needed including developing clinical guidance and external validation.

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“…Another example of the promise and challenges of AI implementation in psychiatric population health is the REACH VET program at the US Veterans Affairs (111,112). The VA has leveraged its immense and harmonized EHR system to investigate a new ML-based program that identifies high suicide risk individuals.…”

Section: Clinical Challengesmentioning

confidence: 99%

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Lee

Torous

Choudhury

et al. 2021

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

150

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Integrating Predictive Modeling Into Mental Health Care: An Example in Suicide Prevention

Cited by 37 publications

References 9 publications

Preventing suicide, promoting resilience: Is this achievable from a global perspective?

Preventing suicide, promoting resilience: Is this achievable from a global perspective?

Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

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