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. 2017 Feb 10:2016:1274-1282.
eCollection 2016.

Moderating the Influence of Current Intention to Improve Suicide Risk Prediction

Nawal A Zaher  1 , Christopher D Buckingham  2
Affiliations

Moderating the Influence of Current Intention to Improve Suicide Risk Prediction

Nawal A Zaher et al. AMIA Annu Symp Proc. .

Abstract

When assessors evaluate a person's risk of completing suicide, the person's expressed current intention is one of the most influential factors. However, ifpeople say they have no intention, this may not be true for a number of reasons. This paper explores the reliability of negative intention in data provided by mental-health services using the GRiST decision support system in England. It identifies features within a risk assessment record that can classify a negative statement regarding current intention of suicide as being reliable or unreliable. The algorithm is tested on previously conducted assessments, where outcomes found in later assessments do or do not match the initially stated intention. Test results show significant separation between the two classes. It means suicide predictions could be made more accurate by modifying the assessment process and associated risk judgement in accordance with a better understanding of the person's true intention.

Keywords: Clinical Risk Judgment; Decision Support System; GRiST; Suicide Intention; Suicide Risk.

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Figures

Figure 1.
Figure 1.
Distribution of clinical risk judgment for patients with current intention of “yes” (the top figure) and “no” (the bottom figure).
Figure 2.
Figure 2.
Classifier model for Cue 1, “date of most recent suicide attempt”, showing the Gaussian distribution for each class, Uno and Rno (unreliable and reliable no intention respectively), and the decision threshold for this dimension, based on the cue’s membership grade, MG.
Figure 3.
Figure 3.
Distribution of MG values for cue 1, “most recent suicide attempt”, RHS: MG values of cue 1 for class Uno, LHS: MG values of cue 1 for class Rno.
Figure 4.
Figure 4.
ROC curve showing TPR against FPR for different decision thresholds.
See this image and copyright information in PMC

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