Moderating the Influence of Current Intention to Improve Suicide Risk Prediction

When assessors evaluate a person's risk of completing suicide, the person's expressed current intention is one of the most influential factors. However, if people 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

Introducing a pilot data collection model for real-time evaluation of data redundancy

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage