Machine Learning Based Suicide Ideation Prediction for Military Personnel

Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1

BACKGROUND

Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1.

OBJECTIVE

This study utilized machine learning (ML) algorithms to identify critical variables and developed the prediction model for predicting MDD in patients with NT1.

METHODS

The study included 267 NT1 patients from four sleep centers. The diagnosis of comorbid MDD was based on Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). ML models, including six full models and six compact models, were developed using a training set. The performance of these models was compared in the testing set, and the optimal model was evaluated in the testing set. Various evaluation metrics, such as Area under the receiver operating curve (AUC), precision-recall (PR) curve and calibration curve were employed to assess and compare the performance of the ML models. Model interpretability was demonstrated using SHAP.

RESULT

In the testing set, the logistic regression (LG) model demonstrated superior performance compared to other ML models based on evaluation metrics such as AUC, PR curve, and calibration curve. The top eight features used in the LG model, ranked by feature importance, included social impact scale (SIS) score, narcolepsy severity scale (NSS) score, total sleep time, body mass index (BMI), education years, age of onset, sleep efficiency, sleep latency.

CONCLUSION

The study yielded a straightforward and practical ML model for the early identification of MDD in patients with NT1. A web-based tool for clinical applications was developed, which deserves further verification in diverse clinical settings.

Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment.

Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons

The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on high-dimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%.

Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial

BACKGROUND

Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices.

OBJECTIVE

This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses.

METHODS

This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response.

RESULTS

The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations.

CONCLUSIONS

The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42547.

Prediction of suicidal ideation with associated risk factors among university students in the southern part of Bangladesh: Machine learning approach

BACKGROUND

The prevalence of suicidal ideation has become an urgent issue, particularly among adolescents. The primary objective of this research is to determine the prevalence of suicidal ideation among students in the southern region of Bangladesh and to predict this phenomenon using machine learning (ML) models.

METHODS

The data collection process involved using a simple random sampling technique to gather information from university students located in the southern region of Bangladesh during the period spreading from April 2022 to June 2022. Upon accounting for missing values and non-response rates, the ultimate sample size was determined to be 584, with 51.5 % of participants identifying as male and 48.5 % female.

RESULTS

A significant proportion of students, precisely 19.9 %, reported experiencing suicidal ideation. Most participants were female (77 %) and unmarried (78 %). Within the machine learning (ML) framework, KNN exhibited the highest accuracy score of 91.45 %. In addition, the Random Forest (RF), and Categorical Boosting (CatBoost) algorithms exhibited comparable levels of accuracy, achieving scores of 90.60 and 90.59 respectively.

LIMITATIONS

Using a cross-sectional design in research limits the ability to establish causal relationships.

CONCLUSION

Mental health practitioners can employ the KNN model alongside patients' medical histories to detect those who may be at a higher risk of attempting suicide. This approach enables healthcare professionals to take appropriate measures, such as counselling, encouraging regular sleep patterns, and addressing depression and anxiety, to prevent suicide attempts.

Unveiling Adolescent Suicidality: Holistic Analysis of Protective and Risk Factors Using Multiple Machine Learning Algorithms

Adolescent suicide attempts are on the rise, presenting a significant public health concern. Recent research aimed at improving risk assessment for adolescent suicide attempts has turned to machine learning. But no studies to date have examined the performance of stacked ensemble algorithms, which are more suitable for low-prevalence conditions. The existing machine learning-based research also lacks population-representative samples, overlooks protective factors and their interplay with risk factors, and neglects established theories on suicidal behavior in favor of purely algorithmic risk estimation. The present study overcomes these shortcomings by comparing the performance of a stacked ensemble algorithm with a diverse set of algorithms, performing a holistic item analysis to identify both risk and protective factors on a comprehensive data, and addressing the compatibility of these factors with two competing theories of suicide, namely, The Interpersonal Theory of Suicide and The Strain Theory of Suicide. A population-representative dataset of 173,664 Norwegian adolescents aged 13 to 18 years (mean = 15.14, SD = 1.58, 50.5% female) with a 4.65% rate of reported suicide attempt during the past 12 months was analyzed. Five machine learning algorithms were trained for suicide attempt risk assessment. The stacked ensemble model significantly outperformed other algorithms, achieving equal sensitivity and a specificity of 90.1%, AUC of 96.4%, and AUCPR of 67.5%. All algorithms found recent self-harm to be the most important indicator of adolescent suicide attempt. Exploratory factor analysis suggested five additional risk domains, which we labeled internalizing problems, sleep disturbance, disordered eating, lack of optimism regarding future education and career, and victimization. The identified factors provided stronger support for The Interpersonal Theory of Suicide than for The Strain Theory of Suicide. An enhancement to The Interpersonal Theory based on the risk and protective factors identified by holistic item analysis is presented.

Do the American guideline-based leisure time physical activity levels for civilians benefit the mental health of military personnel?

Backgrounds

This study aimed to clarify the association of American guideline-based leisure time physical activity (PA) level with mental health in 4,080 military personnel in Taiwan.

Methods

The moderate intensity PA level was assessed according to the total running time per week (wk) reported in a self-administered questionnaire over the previous 6  months and was categorized into PA level <150, 150-299, and ≥300  min/wk. Mental stress was assessed by the Brief Symptom Rating Scale (BSRS)-5 for which ≥15 points were classified as great mental stress. Suicide ideation (SI) was graded as 1 for mild, 2 for moderate, and 3 or 4 for severe. Multivariable logistic regression analysis was employed to determine the association between PA and mental health while adjusting for demographics, smoking, alcohol intake, betel nut chewing, and obesity.

Results

As compared to participants with a PA level of <150  min/wk., those with PA levels 150-299  min/wk. and ≥  300  min/wk. had a lower possibility of SI ≥1 [odds ratios (ORs) and 95% confidence intervals (CIs): 0.58 (0.40-0.83) and 0.23 (0.14-0.36), respectively] and SI ≥1 and/or BSRS-5 ≥  15 [ORs: 0.55 (0.39-0.79) and 0.21 (0.13-0.34), respectively]. The possibilities were more significantly lower for SI ≥2 [ORs: 0.37 (0.20-0.68) and 0.10 (0.04-0.26), respectively] and SI ≥2 and/or BSRS-5 ≥  15 [ORs: 0.35 (0.20-0.62) and 0.10 (0.04-0.25), respectively].

Conclusion

Our findings indicate that participating in moderate-intensity leisure time PA level for ≥150  min/wk. may have a positive effect on mental health among military personnel. The impact appears to be even more significant when engaging in a higher PA level of ≥300  min/wk.

Suicide risk classification with machine learning techniques in a large Brazilian community sample

Even though suicide is a relatively preventable poor outcome, its prediction remains an elusive task. The main goal of this study was to develop machine learning classifiers to identify increased suicide risk in Brazilians with common mental disorders. With the use of clinical and sociodemographic baseline data (n = 4039 adult participants) from a large Brazilian community sample, we developed several models (Elastic Net, Random Forests, Naïve Bayes, and ensemble) for the classification of increased suicide risk among individuals with common mental disorders. 1120 participants (27.7%) presented increased suicide risk. The Random Forests model achieved the best AUC ROC (0.814), followed by Naive Bayes (0.798) and Elastic Net (0.773). Sensitivity varied from 0.922 (Naive Bayes) to 0.630 (Random Forests), while specificity varied from 0.792 (Random Forests) to 0.473 (Naive Bayes). The ensemble model presented an AUC ROC of 0.811, sensitivity of 0.899, and specificity of 0.510. Features representing depression symptoms were the most relevant for the classification of increased suicide risk. Some of our models presented good performance metrics in the classification of increased suicide risk in the investigated sample, which can provide the means to early preventive interventions.

Using machine learning approach to predict depression and anxiety among patients with epilepsy in China: A cross-sectional study

BACKGROUND

Anxiety and depression are the most prevalent comorbidities among epilepsy patients. The screen and diagnosis of anxiety and depression are quite important for the management of patients with epilepsy. In that case, the method for accurately predicting anxiety and depression needs to be further explored.

METHODS

A total of 480 patients with epilepsy (PWE) were enrolled in our study. Anxiety and Depressive symptoms were evaluated. Six machine learning models were used to predict anxiety and depression in patients with epilepsy. Receiver operating curve (ROC), decision curve analysis (DCA) and moDel Agnostic Language for Exploration and eXplanation (DALEX) package were used to evaluate the accuracy of machine learning models.

RESULTS

For anxiety, the area under the ROC curve was not significantly different between models. DCA revealed that random forest and multilayer perceptron has the largest net benefit within different probability threshold. DALEX revealed that random forest and multilayer perceptron were models with best performance and stigma had the highest feature importance. For depression, the results were much the same.

CONCLUSIONS

Methods created in this study may offer much help identifying PWE with high risk of anxiety and depression. The decision support system may be valuable for the everyday management of PWE. Further study is needed to test the outcome of applying this system to clinical settings.

Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work

Background

Millions of workers experience work-related ill health every year. The loss of working days often accounts for poor well-being because of discomfort and stress caused by the workplace. The ongoing pandemic and postpandemic shift in socioeconomic and work culture can continue to contribute to adverse work-related sentiments. Critically investigating state-of-the-art technologies, this study identifies the research gaps in recognizing workers’ need for well-being support, and we aspire to understand how such evidence can be collected to transform the workforce and workplace.

Objective

Building on recent advances in sentiment analysis, this study aims to closely examine the potential of social media as a tool to assess workers’ emotions toward the workplace.

Methods

This study collected a large Twitter data set comprising both pandemic and prepandemic tweets facilitated through a human-in-the-loop approach in combination with unsupervised learning and meta-heuristic optimization algorithms. The raw data preprocessed through natural language processing techniques were assessed using a generative statistical model and a lexicon-assisted rule-based model, mapping lexical features to emotion intensities. This study also assigned human annotations and performed work-related sentiment analysis.

Results

A mixed methods approach, including topic modeling using latent Dirichlet allocation, identified the top topics from the corpus to understand how Twitter users engage with discussions on work-related sentiments. The sorted aspects were portrayed through overlapped clusters and low intertopic distances. However, further analysis comprising the Valence Aware Dictionary for Sentiment Reasoner suggested a smaller number of negative polarities among diverse subjects. By contrast, the human-annotated data set created for this study contained more negative sentiments. In this study, sentimental juxtaposition revealed through the labeled data set was supported by the n-gram analysis as well.

Conclusions

The developed data set demonstrates that work-related sentiments are projected onto social media, which offers an opportunity to better support workers. The infrastructure of the workplace, the nature of the work, the culture within the industry and the particular organization, employers, colleagues, person-specific habits, and upbringing all play a part in the health and well-being of any working adult who contributes to the productivity of the organization. Therefore, understanding the origin and influence of the complex underlying factors both qualitatively and quantitatively can inform the next generation of workplaces to drive positive change by relying on empirically grounded evidence. Therefore, this study outlines a comprehensive approach to capture deeper insights into work-related health.

Leveraging unstructured electronic medical record notes to derive population-specific suicide risk models

Electronic medical record (EMR)-based suicide risk prediction methods typically rely on analysis of structured variables such as demographics, visit history, and prescription data. Leveraging unstructured EMR notes may improve predictive accuracy by allowing access to nuanced clinical information. We utilized natural language processing (NLP) to analyze a large EMR note corpus to develop a data-driven suicide risk prediction model. We developed a matched case-control sample of U.S. Department of Veterans Affairs (VA) patients in 2015 and 2016. We randomly matched each case (all patients that died by suicide in that interval, n = 5029) with five controls (patients that remained alive). We processed note corpus using NLP methods and applied machine-learning classification algorithms to output. We calculated area under the curve (AUC) and risk tiers to determine predictive accuracy. NLP-derived models demonstrated strong predictive accuracy. Patients that scored within top 10% of risk model accounted for up to 29% of suicide decedents. NLP-derived model compares positively to other leading prediction methods. Our approach is highly implementable, only requiring access to text data and open-source software. Additional studies should evaluate ensemble models incorporating NLP-derived information alongside more typical structured variables.

Machine learning-based predictive modeling of depression in hypertensive populations

We aimed to develop prediction models for depression among U.S. adults with hypertension using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms of the developed models. This cross-sectional study included 8,628 adults with hypertension (11.3% with depression) from the National Health and Nutrition Examination Survey (2011–2020). We selected several significant features using feature selection methods to build the models. Data imbalance was managed with random down-sampling. Six different ML classification methods implemented in the R package caret—artificial neural network, random forest, AdaBoost, stochastic gradient boosting, XGBoost, and support vector machine—were employed with 10-fold cross-validation for predictions. Model performance was assessed by examining the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1-score. For an interpretable algorithm, we used the variable importance evaluation function in caret. Of all classification models, artificial neural network trained with selected features (n = 30) achieved the highest AUC (0.813) and specificity (0.780) in predicting depression. Support vector machine predicted depression with the highest accuracy (0.771), precision (0.969), sensitivity (0.774), and F1-score (0.860). The most frequent and important features contributing to the models included the ratio of family income to poverty, triglyceride level, white blood cell count, age, sleep disorder status, the presence of arthritis, hemoglobin level, marital status, and education level. In conclusion, ML algorithms performed comparably in predicting depression among hypertensive populations. Furthermore, the developed models shed light on variables’ relative importance, paving the way for further clinical research.

Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study

Background

Left atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults.

Methods

In a sample of 2,206 male adults aged 17–43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window.

Results

The greatest area under the receiver operating characteristic curve is present in machine learning of the SVM for ECG only (77.87%) and of the MLP for all biological and ECG features (81.01%), both of which are superior to the P wave duration (62.19%). If the sensitivity is fixed to 70–75%, the specificity of the SVM for ECG only is up to 72.4%, and that of the MLP for all biological and ECG features is increased to 81.1%, both of which are higher than 48.8% by the P wave duration.

Conclusions

This study suggests that machine learning is a reliable method for ECG and biological features to predict LAE in young adults. The proposed MLP, LR, and SVM methods provide early detection of LAE in young adults and are helpful to take preventive action on cardiovascular diseases.

Leveraging data science to enhance suicide prevention research: a literature review

OBJECTIVE

The purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.

DESIGN

We conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.

METHODS

For the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.

RESULTS

Results showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.

CONCLUSION

Data science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.

AI enabled suicide prediction tools: a qualitative narrative review

Background: Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services. Advances in artificial intelligence (AI) present opportunities for the development of novel tools for predicting suicide.

Method: We searched Google Scholar and PubMed for articles relating to suicide prediction using artificial intelligence from 2017 onwards.

Conclusions: This paper presents a qualitative narrative review of research focusing on two categories of suicide prediction tools: medical suicide prediction and social suicide prediction. Initial evidence is promising: AI-driven suicide prediction could improve our capacity to identify those at risk of suicide, and, potentially, save lives. Medical suicide prediction may be relatively uncontroversial when it pays respect to ethical and legal principles; however, further research is required to determine the validity of these tools in different contexts. Social suicide prediction offers an exciting opportunity to help identify suicide risk among those who do not engage with traditional health services. Yet, efforts by private companies such as Facebook to use online data for suicide prediction should be the subject of independent review and oversight to confirm safety, effectiveness and ethical permissibility.

Metabolically healthy obesity and physical fitness in military males in the CHIEF study

The metabolically healthy obese (MHO) characterized by the absence of metabolic syndrome have shown superior cardiorespiratory fitness (CRF) and similar muscular strength as compared with the metabolically unhealthy obese (MUO). However, this finding might be biased by the baseline sedentary behavior in the general population. This study utilized 3669 physically active military males aged 18-50 years in Taiwan. Obesity and metabolically unhealthy were respectively defined as body mass index ≥ 27.5 kg/m² and presence of at least two major components of the metabolic syndrome, according to the International Diabetes Federation criteria for Asian male adults. Four groups were accordingly classified as the metabolically healthy lean (MHL, n = 2510), metabolically unhealthy lean (MUL, n = 331), MHO (n = 181) and MUO (n = 647). CRF was evaluated by time for a 3-km run, and muscular strengths were separately assessed by numbers of push-up and sit-up within 2 min. Analysis of covariance was utilized to compare the difference in each exercise performance between groups adjusting for age, service specialty, smoking, alcohol intake and physical activity. The metabolic syndrome prevalence in MUL and MUO was 49.8% and 47.6%, respectively. The performance of CRF did not differ between MHO and MUO (892.3 ± 5.4 s and 892.6 ± 3.0 s, p = 0.97) which were both inferior to MUL and MHL (875.2 ± 4.0 s and 848.6 ± 1.3 s, all p values < 0.05). The performance of muscular strengths evaluated by 2-min push-ups did not differ between MUL and MUO (45.3 ± 0.6 and 45.2 ± 0.4, p = 0.78) which were both less than MHO and MHL (48.4 ± 0.8 and 50.6 ± 0.2, all p values < 0.05). However, the performance of 2-min sit-ups were only superior in MHL (48.1 ± 0.1) as compared with MUL, MHO and MUO (45.9 ± 0.4, 46.7 ± 0.5 and 46.1 ± 0.3, respectively, all p values < 0.05). Our findings suggested that in a physically active male cohort, the MHO might have greater muscle strengths, but have similar CRF level compared with the MUO.

A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.

Psychological stress and long-term blood pressure variability of military young males: The cardiorespiratory fitness and hospitalization events in armed forces study

BACKGROUND

Acute stress might increase short-term heart rate variability and blood pressure variability (BPV); however, chronic stress would not alter short-term BPV in animal models.

AIM

To examine the association of psychological stress with long-term BPV in young male humans.

METHODS

We prospectively examined the association of chronic psychological stress with long-term BPV in 1112 healthy military males, averaged 32.2 years from the cardiorespiratory fitness and hospitalization events in armed forces study in Taiwan. Psychological stress was quantitatively evaluated with the Brief Symptom Rating Scale (BSRS-5), from the least symptom of 0 to the most severe of 20, and the five components of anxiety, insomnia, depression, interpersonal sensitivity, and hostility (the severity score in each component from 0 to 4). Long-term BPV was assessed by standard deviation (SD) for systolic and diastolic blood pressure (SBP and DBP), and average real variability (ARV), defined as the average absolute difference between successive measurements of SBP or DBP, across four visits in the study period from 2012 to 2018 (2012-14, 2014-15, 2015-16, and 2016-18).

RESULTS

The results of multivariable linear regressions showed that there were no correlations of the BSRS-5 score with SD_SBP, SD_DBP, ARV_SBP, and ARV_DBP after adjusting for all the covariates [β(SE): -0.022 (0.024), -0.023 (0.026), -0.001 (0.018), and 0.001 (0.020), respectively; P > 0.05 for all]. In addition, there were also no correlations between each component of the BSRS score and the long-term BPV indexes.

CONCLUSION

Our findings suggest that chronic psychological stress might not be associated with long-term BPV in military young male humans.

Comparisons of traditional electrocardiographic criteria for left and right ventricular hypertrophy in young Asian women: The CHIEF heart study

The performance of electrocardiographic (ECG) voltage criteria to identify left and right ventricular hypertrophy (LVH and RVH) in young Asian female adults have not been clarified so far.In a sample of 255 military young female adults, aged 25.2 years on average, echocardiographic LVH was respectively defined as the left ventricular mass (LVM) indexed by body surface area (BSA) (≥88 g/m) and by height (≥41 g/m), and RVH was defined as anterior right ventricular wall thickness >5.2 mm. The performance of ECG voltage criteria for the echocardiographic LVH and RVH were assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve to estimate sensitivity and specificity.For the Sokolow-Lyon (the maximum of SV1 or SV2 + RV5 or RV6) and Cornell (RaVL + SV3) voltage criteria with the LVM/BSA ≥88 g/m, the AUC of ROC curves were 0.66 (95% confidence intervals [CI]: 0.52-0.81, P = .039) and 0.61 (95% CI: 0.44-0.77, P = .18), respectively. For these 2 ECG voltage criteria with the LVM/height ≥41 g/m, the AUC of ROC curves were 0.64 (95% CI: 0.52-0.75, P = 0.11) and 0.73 (95% CI: 0.61-0.85, P = 0.0074), respectively. The best cut-off points selected for the Sokolow-Lyon and Cornell voltage criteria with echocardiographic LVH in young Asian females were 26 mm and 6 mm, respectively. In contrast, all the AUC of ROC curves were less than 0.60 and not significant according to the Sokolow-Lyon (the maximum of RV1 + SV5 or V6) and Myers' voltage criteria (eg, the voltage of R wave in V1 and the ratios of R/S in V1, V5 and V6) with echocardiographic RVH.There was a suggestion that the ECG voltage criteria to screen the presence of LVH should be adjusted for the young Asian female adults, and with regard to RVH, the ECG voltage criteria were found ineffective.

A 12-Lead ECG-Based System With Physiological Parameters and Machine Learning to Identify Right Ventricular Hypertrophy in Young Adults

OBJECTIVE

The presence of right ventricular hypertrophy (RVH) accounts for approximately 5-10% in young adults. The sensitivity estimated by commonly used 12-lead electrocardiographic (ECG) criteria for identifying the presence of RVH is under 20% in the general population. The aim of this study is to develop a 12-lead ECG system with the related information of age, body height and body weight via machine learning to increase the sensitivity and the precision for detecting RVH.

METHOD

In a sample of 1,701 males, aged 17-45 years, support vector machine is used for the training of 31 parameters including age, body height and body weight in addition to 28 ECG data such as axes, intervals and wave voltages as the inputs to link the output RVH. The RVH is defined on the echocardiographic finding for young males as right ventricular anterior wall thickness > 5.5 mm.

RESULTS

On the system goal for increasing sensitivity, the specificity is controlled around 70-75% and all data tested in the proposed method show competent sensitivity up to 70.3%. The values of area under curve of receiver operating characteristic curve and precision-recall curve using the proposed method are 0.780 and 0.285, respectively, which are better than 0.518 and 0.112 using the Sokolow-Lyon voltage criterion, respectively, for detecting unspecific RVH.

CONCLUSION

We present a method using simple physiological parameters with ECG data to effectively identify more than 70% of the RVH among young adults. Clinical Impact: This system provides a fast, precise and feasible diagnosis tool to screen RVH.