A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

A machine learning algorithm-based model for predicting the risk of non-suicidal self-injury among adolescents in western China: A multicentre cross-sectional study

The prevalence of non-suicidal self-injurious (NSSI) in adolescents is high. However, few studies exist to predict NSSI in this population. This study employed a machine learning algorithm to develop a predictive model, aiming to more accurately assess the risk of NSSI in Chinese adolescents. Sociodemographic, psychological data were collected in 50 schools in western China. We constructed eXtreme Gradient Boosting (XGBoost) model and multivariate logistic regression model to predict the risk of NSSI and nomograms are plotted. Data from 13,304 adolescents were used for model development, with an average age of 13.00 ± 2.17 years; 617 individuals (4.6 %) reported non-suicidal self-injury (NSSI) behaviors. The results of the XGBoost model showed that depression and anxiety were the top two predictors of NSSI in adolescents. The results of the multivariate logistic regression model showed that the risk factors for adolescent NSSI behaviors include: gender (being female), Age, Living with whom (father), History of psychiatric consultation, Stress, Depression, Anxiety, Tolerance, Emotion abreaction. The XGBoost prediction and multivariate logistic regression model showed good predictive ability. Nomograms can serve as clinical tools to assist in intervention measures, helping adolescents reduce NSSI behaviors and improve their mental and physical well-being.

Assessment of machine learning algorithms in national data to classify the risk of self-harm among young adults in hospital: A retrospective study

BACKGROUND

Self-harm is one of the most common presentations at accident and emergency departments in the UK and is a strong predictor of suicide risk. The UK Government has prioritised identifying risk factors and developing preventative strategies for self-harm. Machine learning offers a potential method to identify complex patterns with predictive value for the risk of self-harm.

METHODS

National data in the UK Mental Health Services Data Set were isolated for patients aged 18-30 years who started a mental health hospital admission between Aug 1, 2020 and Aug 1, 2021, and had been discharged by Jan 1, 2022. Data were obtained on age group, gender, ethnicity, employment status, marital status, accommodation status and source of admission to hospital and used to construct seven machine learning models that were used individually and as an ensemble to predict hospital stays that would be associated with a risk of self-harm.

OUTCOMES

The training dataset included 23 808 items (including 1081 episodes of self-harm) and the testing dataset 5951 items (including 270 episodes of self-harm). The best performing algorithms were the random forest model (AUC-ROC 0.70, 95%CI:0.66-0.74) and the ensemble model (AUC-ROC 0.77 95%CI:0.75-0.79).

INTERPRETATION

Machine learning algorithms could predict hospital stays with a high risk of self-harm based on readily available data that are routinely collected by health providers and recorded in the Mental Health Services Data Set. The findings should be validated externally with other real-world, prospective data.

FUNDING

This study was supported by the Midlands and Lancashire Commissioning Support Unit.

Predicting the presence of tinnitus using ecological momentary assessments

Mobile applications have gained popularity in healthcare in recent years. These applications are an increasingly important pillar of public health care, as they open up new possibilities for data collection and can lead to new insights into various diseases and disorders thanks to modern data analysis approaches. In this context, Ecological Momentary Assessment (EMA) is a commonly used research method that aims to assess phenomena with a focus on ecological validity and to help both the user and the researcher observe these phenomena over time. One phenomenon that benefits from this capability is the chronic condition tinnitus. TrackYourTinnitus (TYT) is an EMA-based mobile crowdsensing platform designed to provide more insight into tinnitus by repeatedly assessing various dimensions of tinnitus, including perception (i.e., perceived presence). Because the presence of tinnitus is the dimension that is of great importance to chronic tinnitus patients and changes over time in many tinnitus patients, we seek to predict the presence of tinnitus based on the not directly related dimensions of mood, stress level, arousal, and concentration level that are captured in TYT. In this work, we analyzed a dataset of 45,935 responses to a harmonized EMA questionnaire using different machine learning techniques. In addition, we considered five different subgroups after consultation with clinicians to further validate our results. Finally, we were able to predict the presence of tinnitus with an accuracy of up to 78% and an AUC of up to 85.7%.