Comparisons of different classification algorithms while using text mining to screen psychiatric inpatients with suicidal behaviors

Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review

(1) Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Machine learning and artificial intelligence such as natural language processing has the potential to become a major technique for the detection, diagnosis, and treatment of people. (2) Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. (3) Result: The preliminary search of 5 databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. (4) Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Additionally, to reduce suicides, people with mental problems must be continuously and passively monitored. (5) Conclusions: The use of AI&ML opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention.

Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders

BACKGROUND

Suicide attempt is one of the most severe consequences for patients with mood disorders. This study aimed to perform deep learning and machine learning while using text mining to identify patients with suicide attempts and to compare their effectiveness.

METHODS

A total of 13,100 patients with mood disorders were selected. Two traditional text mining methods, logistic regression and Support vector machine (SVM), and one deep learning model (Convolutional neural network, CNN) were adopted to perform overall analysis and gender-specific subgroup analysis of patients to identify suicide attempts. The classification effectiveness of these models was evaluated by accuracy, F1-value, precision, recall, and the area under Receiver operator characteristic curve (ROC).

RESULTS

CNN's results were greater than the other two for all indicators except recall which was slightly smaller than SVM in male subgroup analysis. The accuracy values of the CNN were 98.4 %, 98.2 %, and 98.5 % in the overall analysis and the subgroup analysis for males and females, respectively. The results of McNemar's test showed that CNN and SVM models' predictions were statistically different from the logistic regression model's predictions in the overall analysis and the subgroup analysis for females (P < 0.050).

LIMITATIONS

A fixed number of features were selected based on document frequency to train models; this was a single-site study.

CONCLUSIONS

CNN model was a better way to detect suicide attempts in patients with mood disorders prior to hospital admission, saving time and resources in recognizing high-risk patients and preventing suicide.

Alcohol and the Risk of Railway Suicide

Suicide is one of the ten most common causes of death in the world. Of all deaths from suicide, 22% can be attributed to the use of alcohol, which means that every fifth suicide would not occur if alcohol were not consumed by the population. People under the influence of alcohol choose more radical and effective methods of dying by suicide, e.g., throwing themselves under a moving vehicle, such as a train. The presented analysis aimed to determine important risk factors affecting railway suicide in Poland and their relation to the state of alcohol intoxication of the victims, and the relationship between ethyl alcohol consumption and the phenomenon of suicide. Documentation obtained from the Department of Forensic Medicine at the Medical University of Warsaw, in the form of death registers and forensic medical records concerning examination and autopsy, was analyzed. This made it possible to identify suicide victims from among pedestrian victims of railway accidents recorded during the period under study. The research was carried out using unidimensional and multidimensional statistical analyses with IBM SPSS Statistics, version 25. Sober suicide victims were statistically significantly older than victims under the influence of alcohol; alcohol concentration was correlated with the age of the victims-the older the victims were, the higher the alcohol concentration. A significantly higher number of deaths attributed to suicide by sober victims was observed in autumn compared to other seasons. Multidimensional analysis showed a statistically significant effect of age and season on the probability of dying by suicide under the influence of alcohol-this probability decreases with the age of the victims and is also significantly lower in autumn. The observed relationship between age and the presence of alcohol in suicide victims can be the cause of railway suicides. Knowledge of the mechanisms of seasonal variability of suicidal behavior can help to develop effective strategies to prevent railway suicides. It is necessary to improve the system of reporting railway suicides, as only reliable statistics provide the possibility of assessing both the scale of the problem and the effectiveness of actions taken.