Data mining of hospital suicidal and self-harm presentation records using a tailored evolutionary algorithm

Nicolas Stapelberg, Marcus Randall, Jerneja Sveticic, Peter Fugelli, Hasmeera Dave, Kathryn Turner

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Abstract

The assessment of outcomes for the Gold Coast Mental Health and Specialist Services Suicide Prevention Strategy implementation required data on suicidal and self-harm presentations to be captured from the Emergency Department Information System (EDIS) database. Suicidal and self-harm presentations are not uniformly coded in the EDIS and require human assessment to differentiate these presentations from other cases (e.g., accidental injuries). A novel evolutionary algorithm was used to learn weighting variables from a psychiatrist-rated training dataset in order to generate an appropriate cut-off score for identifying suicidal and self-harm presentations from EDIS. The resulting Searching EDIS for Records of Suicidal Presentations (SERoSP) program was then run on a psychiatrist-rated validation dataset using the weights generated by the algorithm. SERoSP is optimised to be able to detect suicidal and self harm
presentations with a high degree of accuracy (a sensitivity of 0.95 and a
specificity of 0.92). The SERoSP program is a reliable and cost-effective tool for the identification of suicidal and self-harm presentations from EDIS data, and is currently being successfully used in the suicide prevention strategy evaluation.
Original languageEnglish
Article number100012
Number of pages24
JournalMachine Learning with Applications
Volume3
Early online date25 Nov 2020
DOIs
Publication statusPublished - 15 Mar 2021

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