Self-cutting and prospective repetition of self-harm: studies of emergency department presentations in Ireland

Background Self-harm places an individual at increased risk of future self-harm and suicide, and indicates distress and maladaptive coping. Those who present to hospital with self-cutting form a significant minority of self-harm patients who are at increased risk of prospective repetition of self-harm and suicide compared with those presenting with intentional overdose. In addition to increased risk, there is emerging evidence of demographic, psychological, clinical, and social differences between those presenting with self-cutting and those presenting with overdose. Aim and Key Objectives The aim of the current doctoral work was to examine in detail the association between presenting with self-cutting and risk of prospective repetition. The objectives were: to identify evidence-based risk factors for repetition of self-harm among those presenting to emergency departments with self-harm; to compare demographic and presentation characteristics and prospective repetition across presentations of self-cutting only, self-cutting plus intentional overdose, and intentional overdose only; to compare prospective repetition and other characteristics within self-cutting presentations based on the type of treatment received; to compare self-cutting and intentional overdose patients on psychological risk and protective factors for repetition; and to examine the lived experience of engaging in repeated overdose and self-cutting. Methods The current doctoral work used a mixed-methods approach and is comprised of one systematic review and four empirical studies. The empirical studies were two registry-based prospective studies of Irish hospital presentations of self-harm, one prospective structured interview study, and one qualitative study using Interpretative Phenomenological Analysis. Results The systematic review identified several consistent and emerging risk factors for repetition of self-harm, compared to which self-cutting had a medium-sized effect. The registry studies demonstrated that the involvement of self-cutting, particularly less medically severe selfcutting, confers an increased risk of 1-month and 12-month repetition among Irish index selfharm presentations. The structured psychological study detected higher hopelessness and lower non-reactivity to inner experience among those presenting with self-cutting, and higher depression among those who repeated self-harm. Repeaters had lower baseline levels of protective psychological factors than non-repeaters and continued to have higher depression and hopelessness at follow-up. Finally, the qualitative study indicated that self-harm is a purposeful action taken in response to an overwhelming situation and is evaluated afterwards in terms of personal and social effects. Chosen method of self-harm seemed to be influenced by the desired outcome of the self-harm act, capability, accessibility and previous experience. Conclusion Despite limitations in terms of recruitment rates, the work presented in this thesis is innovative in examining the issue of the association between self-cutting and repetition from multiple perspectives. No one factor can reliably predict all repetition but self-cutting represents one consistent and easily detected risk factor for repetition. Those who present with self-cutting exhibit significant differences on demographic, clinical, and psychological variables compared with those presenting with intentional overdose, and seem to exhibit a more vulnerable profile. However, those who present with self-cutting do not form a discrete or homogenous group, and self-harm methods and levels of suicidal intent are liable to fluctuate over time.

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.