Toward Automatic Risk Assessment to Support Suicide Prevention

Abstract

Abstract. Background: Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice. Aims: We aimed to (a) test our artificial intelligence based, referral-centric methodology in the context of the National Health Service (NHS), (b) determine whether statistically relevant results can be derived from data related to previous suicides, and (c) develop ideas for various exploitation strategies. Method: The analysis used data of patients who died by suicide in the period 2013–2016 including both structured data and free-text medical notes, necessitating the deployment of state-of-the-art machine learning and text mining methods. Limitations: Sample size is a limiting factor for this study, along with the absence of non-suicide cases. Specific analytical solutions were adopted for addressing both issues. Results and Conclusion: The results of this pilot study indicate that machine learning shows promise for predicting within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

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

“…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, ).…”

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

“…It shields against typical methodological pitfalls in data analysis that lead to overfitting and overestimating performance and therefore to misleading results. The novel machine learning algorithms of JADBIO have been validated by the machine learning and statistical community [10][11][12][13][14][15][16][17][18][19][20] , while the system has produced novel results in nanomaterial property predictions 21 , suicide prediction 22 , speech classification 23 , bank failure prediction 24 , function protein prediction 25 , and breast cancer prognosis and drug response prediction 26 , to name a few.…”

Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning

“…For the predictive modeling in this project, we employed the AutoML program JADBio. JADBio has been employed in several other fields to produce novel scientific results (eg, nanomaterial property predictions [ 21 ], suicide prediction [ 30 ], speech classification [ 31 ], bank failure prediction [ 32 ], function protein prediction [ 33 ], and breast cancer prognosis and drug response prediction [ 34 ]). JADBio includes algorithms that are also appropriate for small-sample, high-dimensional biological data, hence the Bio part of the name, but can analyze any type of data that is in a 2-dimensional matrix format, as indicated by the examples provided.…”