Identifying predictors of probable posttraumatic stress disorder in children and adolescents with earthquake exposure: A longitudinal study using a machine learning approach

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Association between patterns of biological rhythm and self-harm: evidence from the baoxing youth mental health (BYMH) cohort

BACKGROUND

Self-harm, a severe mental health concern among children and adolescents, has varying global prevalence rates. Previous studies have suggested potential associations between specific behavioral aspects of biological rhythm and self-harm risk in these populations.

OBJECTIVE

Our study aimed to elucidate the relationship between biological rhythm patterns and the propensity of self-harm among Chinese children and adolescents using the Baoxing Youth Mental Health (BYMH) cohort.

METHODS

We included 1883 Chinese children and adolescents from the BYMH cohort. The self-report questions used to assess biological rhythm and self-harm. We applied Principal Component Analysis (PCA) to distinguish patterns of biological rhythms. Logistic regression models were conducted to estimate the associations between biological rhythm, as well as biological rhythm patterns and risk of self-harm.

RESULTS

Of the participants, 35.0% reported experiencing lifetime self-harm. PCA revealed six significantly predominant biological rhythm patterns. Elevated risks of self-harm were linked with unhealthy eating practices, daytime tiredness, and unhealthy bedtime snacking. Conversely, patterns emphasizing physical exercise, family meals for breakfast, and nutritious diet exhibited decreased self-harm propensities. These trends persisted across varied self-harm attributes, including type, recency, and frequency of self-harm.

CONCLUSIONS

This study underscores the critical impact of biological rhythms on self-harm risks among Chinese youth. Targeted lifestyle interventions, focusing on improved sleep and dietary habits, could serve as potent preventive measures. Our findings lay the groundwork for future longitudinal studies to further probe these associations, fostering the creation of tailored interventions to curb self-harm and enhance mental well-being in younger populations.

Pediatrics in Artificial Intelligence Era: A Systematic Review on Challenges, Opportunities, and Explainability

BACKGROUND

The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, the practical application of AI still faces a number of key challenges. Consequently, there is a requirement for a concise overview of the roles of AI across the multiple domains of pediatric medicine, which the current study seeks to address.

AIM

To systematically assess the challenges, opportunities, and explainability of AI in pediatric medicine.

METHODOLOGY

A systematic search was carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, and grey literature using search terms related to machine learning (ML) and AI for the years 2016 to 2022 in the English language. A total of 210 articles were retrieved that were screened with PRISMA for abstract, year, language, context, and proximal relevance to research aims. A thematic analysis was carried out to extract findings from the included studies.

RESULTS

Twenty articles were selected for data abstraction and analysis, with three consistent themes emerging from these articles. In particular, eleven articles address the current state-of-the-art application of AI in diagnosing and predicting health conditions such as behavioral and mental health, cancer, syndromic and metabolic diseases. Five articles highlight the specific challenges of AI deployment in pediatric medicines: data security, handling, authentication, and validation. Four articles set out future opportunities for AI to be adapted: the incorporation of Big Data, cloud computing, precision medicine, and clinical decision support systems. These studies collectively critically evaluate the potential of AI in overcoming current barriers to adoption.

CONCLUSION

AI is proving disruptive within pediatric medicine and is presently associated with challenges, opportunities, and the need for explainability. AI should be viewed as a tool to enhance and support clinical decision-making rather than a substitute for human judgement and expertise. Future research should consequently focus on obtaining comprehensive data to ensure the generalizability of research findings.

Safety analysis for integrity enhancement in nuclear power plants (NPPs) in case of seashore region site

It is investigated for the seismic consequences in the nuclear power plant (NPP) where the radiological hazard could be one of critical issues when the safety system is in failure. The artificial learning is done during the calculations of each time step. There are the simulations for the artificial neural networking (ANN) as the precision, sensitivity (recall value), specificity, and accuracy which are 21.48%, 50.53%, 25.47%, and 32.68% respectively. Likewise, the recurrent neural network (RNN) modeling has 23.64%, 54.53%, 25.56%, and 34.17% respectively. In the comparisons for ANN and RNN, the values of ANN’s parameters are lower than those of RNN in all values of precision, recall, specificity, and accuracy. As the designed factors for the nuclear matters increase, the estimations could be better in considering the conditional situations.

Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier

BACKGROUND

Depression and post-traumatic stress disorder (PTSD) are among the most common psychiatric disorders observed in children and adolescents exposed to sexual abuse.

OBJECTIVE

The present study aimed to investigate the effects of many factors such as the characteristics of a child, abuse, and the abuser, family type of the child, and the role of social support in the development of psychiatric disorders using machine learning techniques.

PARTICIPANTS AND SETTINGS

The records of 482 children and adolescents who were determined to have been sexually abused were examined to predict the development of depression and PTSD.

METHODS

Each child was evaluated by a child and adolescent psychiatrist in the psychiatric aspect according to the DSM-V. Through the data of both groups, a predictive model was established based on a random forest classifier.

RESULTS

The mean values and standard deviation of the 10-k cross-validated results were obtained as accuracy: 0.82% (+/- 0.19%), F1: 0.81% (+/- 0.19%), precision: 0.81% (+/- 0.19%), recall: 0.80% (+/- 0.19%) for children with depression; and accuracy: 0.72% (+/- 0.12%), F1: 0.71% (+/- 0.12%), precision: 0.72% (+/- 0.12%), recall: 0.71% (+/- 0.12%) for children with PTSD, respectively. ROC curves were drawn for both, and the AUC results were obtained as 0.88 for major depressive disorder and 0.76 for PTSD.

CONCLUSIONS

Machine learning techniques are powerful methods that can be used to predict disorders that may develop after sexual abuse. The results should be supported by studies with larger samples, which are repeated and applied to other risk groups.

Chinese College Students Have Higher Anxiety in New Semester of Online Learning During COVID-19: A Machine Learning Approach

The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study and asked to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale (SAS) twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 (15.7%, Mean = 58.45, SD = 7.81) and 221 (18.86%, Mean = 57.68, SD = 7.58) students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test (P < 0.05). Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies (P < 0.05). The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students.

Disrupted Rhythmicity and Vegetative Functions Relate to PTSD and Gender in Earthquake Survivors

Background: Increasing evidence indicates that survivors to traumatic events may show disruption of sleep pattern, eating and sexual behaviors, and somatic symptoms suggestive of alterations of biorhythmicity and vegetative functions. Therefore, the aim of this study was to investigate these possible alterations in a sample of survivors in the aftermath of earthquake exposure, with particular attention to gender differences and impact of post-traumatic stress disorder (PTSD). Methods: High school senior students, who had been exposed to the 2009 L'Aquila earthquake, were enrolled 21 months after the traumatic event and evaluated by the Trauma and Loss Spectrum Self-Report to investigate PTSD rates and by a domain of the Mood Spectrum Self-Report-Lifetime Version (MOODS-SR), to explore alterations in circadian/seasonal rhythms and vegetative functions. Results: The rates of endorsement of MOODS-SR rhythmicity and vegetative functions domain and subdomain scores were significantly higher in survivors with PTSD with respect to those without it. Among all earthquake survivors, women reported higher scores than men on the rhythmicity and vegetative functions domain and subdomain scores, except for the rhythmicity and sexual functions ones. Female survivors without PTSD showed significantly higher scores than men in the rhythmicity and vegetative functions total scores and the sleep and weight and appetite subdomains. Potentially traumatic events burden predicted rhythmicity and vegetative functions impairment, with a moderation effect of re-experiencing symptoms. Conclusions: We report impairments in rhythmicity, sleep, eating, and sexual and somatic health in survivors to a massive earthquake, particularly among subjects with PTSD and higher re-experiencing symptoms, with specific gender-related differences. Evaluating symptoms of impaired rhythmicity and vegetative functions seems essential for a more accurate assessment and clinical management of survivors to a mass trauma.