This study was conducted in accordance with the principles of the Declaration of Helsinki and the Japanese Ethical Guidelines for Medical and Health Research Involving Human Subjects. Before the initiation of the study, the study protocol was reviewed and approved by the University of Occupational and Environmental Health, Japan (approval number: H30-093). These ethics committees also exempted us from obtaining informed consent because this study is a retrospective medical record survey. Instead, we published information about this study to give patients the freedom to opt-out. To protect patients’ personal information, we assigned each patient an arbitrary identification number for this study.
Study Design and Participants
This study is a retrospective observational study using electronic medical record information. Patients who attempted suicide and visited the emergency department of our hospital from January 2015 to April 2018 were enrolled in the study. Extracted from disease names, out of 143 suicide cases, patients, who attempted suicide through other means, such as wrist cutting, hanging, and jumping, were excluded, and 101 patients who overdosed on psychotic drugs were included in the study. The participants in the present study overlapped with those in our past published study ; however, no study has analyzed association rule analysis.
Demographic and Clinical Assessment
Participants’ demographic and clinical characteristics were assessed by the SAD PERSONS scale , and the assessment score was evaluated immediately after the visit. If the SAD PERSONS score was not assessed on the day of the visit, it was assessed at a later date if it could be inferred retrospectively from the medical record.
All statistical analyses were performed by Python ver. 3.0 , and we used association rules analysis. In analysis, we used the apriori-algorithm and excluded the set of a priori unnecessary items from the calculation. The apriori-algorithm method is efficient in finding frequent itemsets from extensive data .
We first used the apriori-algorithm to select single products or combinations with a support value of at least 0.1. Second, we set the lift value to be at least 1.0. Lift value is a performance measure of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (concerning the population as a whole), measured against a random choice targeting model. Briefly, it is a measure of the degree to which items X and Y appear simultaneously after accounting for their occurrence frequency. The lift value is based on 1.0. If it is higher than 1.0, item X and item Y are more likely to occur simultaneously, and vice versa if lower than 1.0.
The network association graph was drawn using networkx, a Python library. The size of the nodes was adjusted to be proportional to the number of degrees. Vector orientation was drawn in both directions. Data were expressed as mean ± standard deviation.