Identifying the suicidal ideation risk group among older adults in rural areas: Developing a predictive model using machine learning methods

Abstract

Aims The aim of this study was to develop a predictive model that can identify the suicidal ideation risk group among older adults in rural areas using machine learning methods. Design This study applied an exploratory, descriptive and cross‐sectional design. Methods The participants were older adults (N = 650) aged over 65 living in rural areas of South Korea. Self‐report questionnaires were used to collect the demographics, suicidal ideation, depression, socioeconomic information and basic health information from September to October 2020. The collected data were analysed using machine learning methods with R statistical software 4.1.0. Results The predictive models indicated that depression, pain, age and loneliness were significant factors of suicidal ideation. Good performance was observed based on the area under the receiver operating characteristic curve in the decision tree, random forest and logistic regression. Finally, the evaluation of model performance indicated moderate to high sensitivity and specificity. Conclusion The predictive models using machine learning methods may be useful to predict the risk of suicidal ideation. Furthermore, depression with pain, age and feelings of loneliness should be included in the initial screening to assess suicide risk among older adults in rural areas. Impact Identifying suicidal risk among older adults is challenging. Thus, employing predictive models that can assess depression, pain, age and loneliness can enable public healthcare providers to detect suicidal risk groups. Particularly, the presented models from this study can facilitate healthcare providers with initiating early interventions to prevent suicide among older adults in clinical and community nursing care settings. Reporting Method The reporting of this study (Observational, cross‐sectional study) conforms to the STROBE statement. Patient or Public Contribution No patient or public contribution. This study did not involve patients, service users, caregivers or members of the public. Implication for the Profession and/or Patients Care Applying this model may help to prevent geriatric suicide because the nursing staff will have a greater awareness regarding the suicide ideation risk of older adults, thereby reducing the possibility of their suicide.

What are ‘emerging methodologies’ in nursing research? Our perspectives as early career researchers

“…These tactical methodological approaches may hold the key to improving health outcomes for patients and job outcomes for nurses. The use of artificial intelligence such as machine learning (Kim et al, 2023) and natural language processing (Song et al, 2023) are examples of approaches in nursing research published in JAN that can help make sense of big data in nursing, from the past, present and future, to capture the changing nature of phenomena and create benchmarks needed to improve poorer patient and nurse outcomes. Although the use of AI is an exciting new realm, it behoves researchers to carefully consider the use of AI in processing data and ensure that innovation is the resulting product rather than recycled information.…”

Enhancing knowledge management in nursing through documentation

“…In addition, KM principles and practices contribute to developing effective nursing interventions to promotion quality, safety and efficiency of patient care. Recent studies show that KM has been used as an aid to develop predictive models to support decision‐making, for example for identifying the suicidal ideation risk among older adults (Kim et al, 2023). Implementing sound KM practices can enable strategies such as the use of benchmarks by which patient care outcomes of one unit may be compared with outcomes of other similar units in order to determine best practices.…”