Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: A supervised machine learning approach

Speech-based recognition and estimating severity of PTSD using machine learning

BACKGROUND

Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily rely on interviews, incurring considerable costs and lacking objective indices. Integrating biomarkers and machine learning techniques into this diagnostic process has the potential to facilitate accurate PTSD assessment by clinicians.

METHODS

We assembled a dataset encompassing recordings from 76 individuals diagnosed with PTSD and 60 healthy controls. Leveraging the openSmile framework, we extracted acoustic features from these recordings and employed a random forest algorithm for feature selection. Subsequently, these selected features were utilized as inputs for six distinct classification models and a regression model.

RESULTS

Classification models employing a feature set of 18 elements yielded robust binary prediction outcomes for PTSD. Notably, the RF model achieved peak accuracy at 0.975 with the highest AUC of 1.0. In terms of the regression model, it exhibited significant predictive capability for PCL-5 scores (MSE = 0.90, MAE = 0.76, R2 = 0.10, p < 0.001). Noteworthy was the correlation coefficient of 0.33 (p < 0.01) between predicted and actual values.

LIMITATIONS

Firstly, the process of feature selection may compromise the stability of models, which leads to potentially overestimating results. Secondly, it is hard to elucidate the nature of biological mechanisms behind between PTSD patients and healthy individuals. Lastly, the regression model has a limited prediction for PTSD.

CONCLUSIONS

Distinct speech patterns differentiate PTSD patients and controls. Classification models accurately discern both groups. Regression model gauges PTSD severity, but further validation on larger datasets is needed.

Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review

BACKGROUND

Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.

OBJECTIVE

This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.

METHODS

Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.

RESULTS

A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.

CONCLUSIONS

The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

Applications of artificial intelligence-machine learning for detection of stress: a critical overview

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review

The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.

Prediction of Chinese clients' satisfaction with psychotherapy by machine learning

Background

Effective psychotherapy should satisfy the client, but that satisfaction depends on many factors. We do not fully understand the factors that affect client satisfaction with psychotherapy and how these factors synergistically affect a client's psychotherapy experience.

Aims

This study aims to use machine learning to predict Chinese clients' satisfaction with psychotherapy and analyze potential outcome contributors.

Methods

In this cross-sectional investigation, a self-compiled online questionnaire was delivered through the WeChat app. The information of 791 participants who had received psychotherapy was used in the study. A series of features, for example, the participants' demographic features and psychotherapy-related features, were chosen to distinguish between participants satisfied and dissatisfied with the psychotherapy they received. With our dataset, we trained seven supervised machine-learning-based algorithms to implement prediction models.

Results

Among the 791 participants, 619 (78.3%) reported being satisfied with the psychotherapy sessions that they received. The occupation of the clients, the location of psychotherapy, and the form of access to psychotherapy are the three most recognizable features that determined whether clients are satisfied with psychotherapy. The machine-learning model based on the CatBoost achieved the highest prediction performance in classifying satisfied and psychotherapy clients with an F1 score of 0.758.

Conclusion

This study clarified the factors related to clients' satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes.

"After Philando, I Had to Take a Sick Day to Recover": Psychological Distress, Trauma and Police Brutality in the Black Community

Unfortunately, encounters between Black Americans and law enforcement frequently feature violence, resulting in a myriad of negative health influences including increased rates of race-based mortality, chronic stress and trauma. Despite police brutality's increased media coverage, there is a dearth of empirical research surrounding violence at the hands of law enforcement and how it impacts the health of the Black community. Police departments across the country fail to release official data or information related to police brutality; however, technological advancements, such as social media and smart phones, have revolutionized information seeking and dissemination related to brutality. Drawing on Critical Race Theory (CRT), this qualitative study used phenomenological interviews to better understand how Black Americans interpret stories of police brutality and the impact such stories have on one's mental health. Analysis confirmed previous quantitative research, revealing the Black community can be traumatized by viewing graphic images or videos of police brutality. Interviewees reported a constant fear of dying, hyper alertness and lack of coping mechanisms. Findings from this study not only should be used to build health communication models that can address mental health issues, but also should be used as a blueprint to improve police-community relations. Implications are drawn from this study for addressing the issue of police violence in the Black community. Policy recommendations are suggested.

Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data

Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments.

Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection

Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of sensor and reference data during the development phase. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. These questionnaires only capture stress levels at a specific point in time. Moreover, self-reporting is subjective and prone to inaccuracies. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress.

Understanding Heart Rate Reactions to Post-Traumatic Stress Disorder (PTSD) Among Veterans: A Naturalistic Study

OBJECTIVE

We collected naturalistic heart rate data from veterans diagnosed with post-traumatic stress disorder (PTSD) to investigate the effects of various factors on heart rate.

BACKGROUND

PTSD is prevalent among combat veterans in the United States. While a positive correlation between PTSD and heart rate has been documented, specific heart rate profiles during the onset of PTSD symptoms remain unknown.

METHOD

Veterans were recruited during five cycling events in 2017 and 2018 to record resting and activity-related heart rate data using a wrist-worn device. The device also logged self-reported PTSD hyperarousal events. Regression analyses were performed on demographic and behavioral covariates including gender, exercise, antidepressants, smoking habits, sleep habits, average heart rate during reported hyperarousal events, age, glucocorticoids consumption, and alcohol consumption. Heart rate patterns during self-reported PTSD hyperarousal events were analyzed using Auto Regressive Integrated Moving Average (ARIMA). Heart rate data were also compared to an open-access non-PTSD representative case.

RESULTS

Of 99 veterans with PTSD, 91 participants reported at least one hyperarousal event, with a total of 1023 events; demographic information was complete for 38 participants who formed the subset for regression analyses. The results show that factors including smoking, sleeping, gender, and medication significantly affect resting heart rate. Moreover, unique heart rate patterns associated with PTSD symptoms in terms of stationarity, autocorrelation, and fluctuation characteristics were identified.

CONCLUSION

Our findings show distinguishable heart rate patterns and characteristics during PTSD hyperarousal events.

APPLICATION

These findings show promise for future work to detect the onset of PTSD symptoms.

TOXGB: Teamwork Optimization Based XGBoost model for early identification of post-traumatic stress disorder

Post-traumatic stress (PTSD) is considered a clinical issue that influences numerous people from diverse trades all over the world. Numerous research scholars recorded diverse complexities to estimate the severity of the PTSD symptoms in the patients. But diagnosing PTSD and obtaining accurate diagnosing techniques becomes a more complicated task. Therefore, this paper develops a speech based post-traumatic stress disorder monitoring method and the significant objective of the proposed method is to determine if the patients are affected by PTSD. The proposed approach utilizes three different steps: pre-processing or pre-emphasis, feature extraction as well as classification to evaluate the patients affected by PTSD or not. The input speech signal is initially provided to the pre-processing phase where the speech gets segmented into frames. The speech frame is then extracted and classified using XGBoost based Teamwork optimization (XGB-TWO) algorithm. In addition to this, we utilized two different types of datasets namely TIMIT and FEMH to evaluate and classify the PSTD from the speech signals. Furthermore, based on the evaluation of the proposed model to diagnose PTSD patients, various evaluation metrics namely accuracy, specificity, sensitivity, and recall are evaluated. Finally, the experimental investigation and comparative analysis are carried out and the evaluation results demonstrated that the accuracy rate achieved for the proposed technique is 98.25%.

Diagnosing post-traumatic stress disorder using electronic medical record data

This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model's ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.

A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings

Artificial Intelligence (AI) is one of the hottest topics in our society, especially when it comes to solving data-analysis problems. Industry are conducting their digital shifts, and AI is becoming a cornerstone technology for making decisions out of the huge amount of (sensors-based) data available in the production floor. However, such technology may be disappointing when deployed in real conditions. Despite good theoretical performances and high accuracy when trained and tested in isolation, a Machine-Learning (M-L) model may provide degraded performances in real conditions. One reason may be fragility in treating properly unexpected or perturbed data. The objective of the paper is therefore to study the robustness of seven M-L and Deep-Learning (D-L) algorithms, when classifying univariate time-series under perturbations. A systematic approach is proposed for artificially injecting perturbations in the data and for evaluating the robustness of the models. This approach focuses on two perturbations that are likely to happen during data collection. Our experimental study, conducted on twenty sensors’ datasets from the public University of California Riverside (UCR) repository, shows a great disparity of the models’ robustness under data quality degradation. Those results are used to analyse whether the impact of such robustness can be predictable—thanks to decision trees—which would prevent us from testing all perturbations scenarios. Our study shows that building such a predictor is not straightforward and suggests that such a systematic approach needs to be used for evaluating AI models’ robustness.

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

Improving Psychiatric Care Through Integrated Digital Technologies

This manuscript provides an overview of our efforts to implement an integrated electronic monitoring and feedback platform to increase patient engagement, improve care delivery and outcome of treatment, and alert care teams to deterioration in functioning. Patients First utilizes CareSense, a digital care navigation and data collection system, to integrate traditional patient-reported outcomes monitoring with novel biological monitoring between visits to provide patients and caregivers with real-time feedback on changes in symptoms such as stress, anxiety, and depression. The next stage of project development incorporates digital therapeutics (computerized therapeutic interventions) for patients, and video resources for primary care physicians and nurse practitioners who serve as the de facto front line for psychiatric care. Integration of the patient-reported outcomes monitoring with continuous biological monitoring, and digital supports is a novel application of existing technologies. Video resources pushed to care providers whose patients trigger a symptom severity alert is, to our knowledge, an industry first.

Ecological momentary assessment (EMA) of mental health outcomes in veterans and servicemembers: A scoping review

This PRISMA scoping review explores existing research conducted with United States military samples utilizing ecological momentary assessment (EMA) to evaluate mental health outcomes. EMA facilitates understanding of temporal changes of dynamic variables subject to change difficult to capture in standard laboratory assessment. It also elucidates understanding of complex etiology of mental illness in military and veteran samples and treatment approaches. Thirty-two articles published between 1995 and 2019 met inclusion criteria. Most (68.7%) included studies examined mental health symptoms and their temporal relationship to other outcomes among servicemembers and/or veterans, particularly posttraumatic stress disorder and substance use disorders. EMA was frequently employed to better understand underlying mechanisms of mental illness, predict symptom changes, assess feasibility among special populations, and assess treatment outcomes. Considerable variability existed in assessment period duration, number of daily assessments, and EMA modalities utilized. Several research gaps were identified, including underutilization of EMA to study suicide risk in veterans/servicemembers. EMA has great potential for increasing understanding of an array of complex mental health problems; however, this highly promising approach has been largely underutilized to study mental health issues among veteran and military populations to date, perhaps due to institutional delays in its adoption secondary to privacy/data security concerns.

Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

BACKGROUND

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric condition that is associated with symptoms such as hyperarousal and overreactions. Treatments for PTSD are limited to medications and in-session therapies. Assessing the way the heart responds to PTSD has shown promise in detecting and understanding the onset of symptoms.

OBJECTIVE

This study aimed to extract statistical and mathematical approaches that researchers can use to analyze heart rate (HR) data to understand PTSD.

METHODS

A scoping literature review was conducted to extract HR models. A total of 5 databases including Medical Literature Analysis and Retrieval System Online (Medline) OVID, Medline EBSCO, Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO, Excerpta Medica Database (Embase) Ovid, and Google Scholar were searched. Non-English language studies, as well as studies that did not analyze human data, were excluded. A total of 54 studies that met the inclusion criteria were included in this review.

RESULTS

We identified 4 categories of models: descriptive time-independent output, descriptive and time-dependent output, predictive and time-independent output, and predictive and time-dependent output. Descriptive and time-independent output models include analysis of variance and first-order exponential; the descriptive time-dependent output model includes a classical time series analysis and mixed regression. Predictive time-independent output models include machine learning methods and analysis of the HR-based fluctuation-dissipation method. Finally, predictive time-dependent output models include the time-variant method and nonlinear dynamic modeling.

CONCLUSIONS

All of the identified modeling categories have relevance in PTSD, although the modeling selection is dependent on the specific goals of the study. Descriptive models are well-founded for the inference of PTSD. However, there is a need for additional studies in this area that explore a broader set of predictive models and other factors (eg, activity level) that have not been analyzed with descriptive models.

Burnout syndrome in Romanian medical residents in time of the COVID-19 pandemic

Burnout is a state of physical or mental collapse caused by overwork or stress. Burnout during residency training has gained significant attention secondary to concerns regarding job performance and patient care. The new COVID-19 pandemic has raised public health problems around the world and required a reorganization of health services. In this context, burnout syndrome and physical exhaustion have become even more pronounced. Resident doctors, and especially those in certain specialties, seem even more exposed due to the higher workload, prolonged exposure and first contact with patients. This article is a short review of the literature and a presentation of some considerations regarding the activity of the medical residents in a non-Covid emergency hospital in Romania, based on the responses obtained via a questionnaire. Burnout prevalence is not equal in different specialties. We studied its impact and imagine the potential steps that can be taken in order to reduce the increasing rate of burnout syndrome in the pandemics.

Veteran-Centered Investigation of Architectural and Space Design Considerations for Post-Traumatic Stress Disorder (PTSD)

AIM

This study sought to investigate architectural and space design considerations for veterans with post-traumatic stress disorder (PTSD).

BACKGROUND

Anecdotal evidence suggests that urban design features could have a positive impact on the mental well-being of individuals suffering from PTSD. However, evidence-based architectural and space design guidelines for PTSD are largely absent.

METHODS

Semi-structured interviews were conducted with 17 veterans diagnosed with PTSD to gain insights into their personal experiences with physical indoor and outdoor spaces, and to inquire about their needs and expectations for future architectural design. Transcripts were analyzed thematically.

RESULTS

Architectural design features including windows, entrances and exits, walkways and hallways, open space, defensible space, and green space; interior design features including furnishings and color; and ambient features including light, air quality, and noise levels were identified as most influential design features.

CONCLUSIONS

Our results underscore the first important step to developing comprehensive architectural and space design guidelines for veterans with PTSD. Work is in progress to solicit more feedback from veterans.