Symptom mapping and personalized care for depression, anxiety and stress: A data-driven AI approach

BACKGROUND

Depression, anxiety, and stress disorders have significant and widespread impacts worldwide, affecting millions of individuals and their communities. According to the World Health Organization, depression impacts the daily lives of more than 300 million people, making it one of the most important diseases globally. Treatment for these mental disorders (MD) typically involves medication and psychotherapies, but also incorporates technological resources like Artificial Intelligence (AI) to indicate personalized therapies and care. While various AI approaches have been applied in the context of MD in the literature, they often focus solely on aiding diagnosis.

OBJECTIVE

This research proposes an AI approach for mapping symptoms and assisting in the personalized care of depression, anxiety, and stress.

METHODS

Symptom mapping utilizes data mining (DM) techniques to generate rules representing knowledge extracted from data of 242 patients collected using the Depression, Anxiety, and Stress Scale (DASS-21). This knowledge elucidates how symptoms impact the severity degrees of considered MDs. Subsequently, the generated rules are employed to construct a Fuzzy Inference System (FIS) for inferring the severities of MDs based on patient symptoms and personal data.

RESULTS AND CONCLUSIONS

The results achieved in the DM (accuracy ≥92.98 %, sensibility ≥86.02 %, specificity ≥97.32 %, and kappa statistic ≥87.98 %), indicating consistent patterns, along with the results produced by the FIS, demonstrate the potential of the proposed approach to assist health professionals in rapidly predicting symptoms of depression, anxiety, and stress, thereby facilitating outpatient screening and emergency care. Furthermore, it can improve the association of symptoms, referral to specialized care, therapeutic proposals, and even investigations of other diseases unrelated to MD.

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.

Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study

BACKGROUND

Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders.

METHODS

We applied ML to the data from the Canadian Longitudinal Study on Aging (CLSA) to predict the onset of anxiety disorders approximately three years in the future. We used Shapley value-based methods to determine the top factor for prediction. We also investigated whether anxiety onset can be predicted by baseline depression-related predictors alone.

RESULTS

Our model was able to predict anxiety onset accurately (Area under the Receiver Operating Characteristic Curve or AUC = 0.814 ± 0.016 (mean ± standard deviation), balanced accuracy = 0.741 ± 0.016, sensitivity = 0.743 ± 0.033, and specificity = 0.738 ± 0.010). The top predictive factors included prior depression or mood disorder diagnosis, high frailty, anxious personality, and low emotional stability. Depression and mood disorders are well known comorbidity of anxiety; however a prior depression or mood disorder diagnosis could not predict anxiety onset without other factors.

LIMITATION

While our findings underscore the importance of a prior depression diagnosis in predicting anxiety, they also highlight that it alone is inadequate, signifying the necessity to incorporate additional predictors for improved prediction accuracy.

CONCLUSION

Our study showcases promising prospects for using machine learning to develop personalized prediction models for anxiety onset in middle-aged and older adults using easy-to-access survey data.

Detecting depression severity using weighted random forest and oxidative stress biomarkers

This study employs machine learning to detect the severity of major depressive disorder (MDD) through binary and multiclass classifications. We compared models that used only biomarkers of oxidative stress with those that incorporate sociodemographic and health-related factors. Data collected from 830 participants, based on the Patient Health Questionnaire (PHQ-9) score, inform our analysis. In binary classification, the Random Forest (RF) classifier achieved the highest Area Under the Curve (AUC) of 0.84 when all features were included. In multiclass classification, the AUC improved from 0.84 with only oxidative stress biomarkers to 0.88 when all characteristics were included. To address data imbalance, weighted classifiers, and Synthetic Minority Over-sampling Technique (SMOTE) approaches were applied. Weighted random forest (WRF) improved multiclass classification, achieving an AUC of 0.91. Statistical tests, including the Friedman test and the Conover post-hoc test, confirmed significant differences between model performances, with WRF using all features outperforming others. Feature importance analysis shows that oxidative stress biomarkers, particularly GSH, are top ranked among all features. Clinicians can leverage the results of this study to improve their decision-making processes by incorporating oxidative stress biomarkers in addition to the standard criteria for depression diagnosis.

A novel approach to anxiety level prediction using small sets of judgment and survey variables

Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2-3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29-31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.

Artificial intelligence in preventive cardiology

Artificial intelligence (AI) is a field of study that strives to replicate aspects of human intelligence into machines. Preventive cardiology, a subspeciality of cardiovascular (CV) medicine, aims to target and mitigate known risk factors for CV disease (CVD). AI's integration into preventive cardiology may introduce novel treatment interventions and AI-centered clinician assistive tools to reduce the risk of CVD. AI's role in nutrition, weight loss, physical activity, sleep hygiene, blood pressure, dyslipidemia, smoking, alcohol, recreational drugs, and mental health has been investigated. AI has immense potential to be used for the screening, detection, and monitoring of the mentioned risk factors. However, the current literature must be supplemented with future clinical trials to evaluate the capabilities of AI interventions for preventive cardiology. This review discusses present examples, potentials, and limitations of AI's role for the primary and secondary prevention of CVD.

Using cross-validation methods to select time series models: Promises and pitfalls

Vector autoregressive (VAR) modelling is widely employed in psychology for time series analyses of dynamic processes. However, the typically short time series in psychological studies can lead to overfitting of VAR models, impairing their predictive ability on unseen samples. Cross-validation (CV) methods are commonly recommended for assessing the predictive ability of statistical models. However, it is unclear how the performance of CV is affected by characteristics of time series data and the fitted models. In this simulation study, we examine the ability of two CV methods, namely,10-fold CV and blocked CV, in estimating the prediction errors of three time series models with increasing complexity (person-mean, AR, and VAR), and evaluate how their performance is affected by data characteristics. We then compare these CV methods to the traditional methods using the Akaike (AIC) and Bayesian (BIC) information criteria in their accuracy of selecting the most predictive models. We find that CV methods tend to underestimate prediction errors of simpler models, but overestimate prediction errors of VAR models, particularly when the number of observations is small. Nonetheless, CV methods, especially blocked CV, generally outperform the AIC and BIC. We conclude our study with a discussion on the implications of the findings and provide helpful guidelines for practice.

AI-Enhanced Predictive Modeling for Identifying Depression and Delirium in Cardiovascular Patients Scheduled for Cardiac Surgery

Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional factors. Addressing this issue, our study pioneers a straightforward, explainable, and data-driven pipeline for predicting depression in CVD patients.

METHODS

Our study was conducted at a cardiac surgical intensive care unit. A total of 224 participants who were scheduled for elective coronary artery bypass graft surgery (CABG) were enrolled in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder (MDD) based on the DSM-5 criteria. An advanced data curation workflow was applied to eliminate outliers and inconsistencies and improve data quality. An explainable AI-empowered pipeline was developed, where sophisticated machine learning techniques, including the AdaBoost, random forest, and XGBoost algorithms, were trained and tested on the curated data based on a stratified cross-validation approach.

RESULTS

Our findings identified a significant correlation between the biomarker "sRAGE" and depression (r = 0.32, p = 0.038). Among the applied models, the random forest classifier demonstrated superior accuracy in predicting depression, with notable scores in accuracy (0.62), sensitivity (0.71), specificity (0.53), and area under the curve (0.67).

CONCLUSIONS

This study provides compelling evidence that depression in CVD patients, particularly those with elevated "sRAGE" levels, can be predicted with a 62% accuracy rate. Our AI-driven approach offers a promising way for early identification and intervention, potentially revolutionizing care strategies in this vulnerable population.

MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review

BACKGROUND

Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used in combination with Electronic Health Records for prediction of depression.

METHODS

Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022).

RESULTS

Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques.

LIMITATIONS

The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography.

CONCLUSION

This review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns with generalizability and interpretability.

A feature selection framework for anxiety disorder analysis using a novel multiview harris hawk optimization algorithm

Machine learning (ML) has demonstrated its ability to exploit important relationships within data collection, which can be used in the diagnosis, treatment, and prediction of outcomes in a variety of clinical contexts. Anxiety mental disorder analysis is one of the pending difficulties that ML can help with. A thorough study is demanded to gain a better understanding of this illness. Since the anxiety data is generally multidimensional, which complicates processing and as a result of technology improvements, medical data from several perspectives, known as multiview data (MVD), is being collected. Each view has its own data type and feature values, so there is a lot of diversity. This work introduces a novel preprocessing feature selection (FS) approach, multiview harris hawk optimization (MHHO), which has the potential to reduce the dimensionality of anxiety data, hence reducing analytical effort. The uniqueness of MHHO originates from combining a multiview linking methodology with the power of the harris hawk optimization (HHO) method. The HHO is used to identify the lowest optimal MVD feature subset, while multiview linking is utilized to find a promising fitness function to direct the HHO FS while accounting for all data views' heterogeneity. The complexity of MHHO is O(THL2), where T is the number of iterations, H is the number of involved harris hawks, and L is the number of objects. Using two publicly available anxiety MVDs, MHHO is validated against ten recent rivals in its category. The experimental findings show that MHHO has a considerable advantage in terms of convergence speed (converging in less than ten iterations), subset size (removing 75% of the views; reducing feature size by 66%), and classification accuracy (approaching 100%). Furthermore, statistical analyses reveal that MHHO is statistically different from its competitors, bolstering its applicability. Finally, feature importance is evaluated, shedding light on the most anxiety-inducing characteristics. The likelihood of developing additional disorders (such as depression or stress) is also investigated.

Machine learning functional impairment classification with electronic health record data

BACKGROUND

Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment.

METHODS

We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome.

RESULTS

Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states.

CONCLUSION

A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.

A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis

Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.

Developing a Multimodal Monitoring System for Geriatric Depression: A Feasibility Study

The Internet of Medical Things is promising for monitoring depression symptoms. Therefore, it is necessary to develop multimodal monitoring systems tailored for elderly individuals with high feasibility and usability for further research and practice. This study comprised two phases: (1) methodological development of the system; and (2) system validation to evaluate its feasibility. We developed a system that includes a smartphone for facial and verbal expressions, a smartwatch for activity and heart rate monitoring, and an ecological momentary assessment application. A sample of 21 older Koreans aged 65 years and more was recruited from a community center. The 4-week data were collected for each participant (n = 19) using self-report questionnaires, wearable devices, and interviews and were analyzed using mixed methods. The depressive group (n = 6) indicated lower user acceptance relative to the nondepressive group (n = 13). Both groups experienced positive emotions, had regular life patterns, increased their self-interest, and stated that a system could disturb their daily activities. However, they were interested in learning new technologies and actively monitored their mental health status. Our multimodal monitoring system shows potential as a feasible and useful measure for acquiring mental health information about geriatric depression.

Clinical decision support system for quality of life among the elderly: an approach using artificial neural network

Background

Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors.

Methods

In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL.

Results

Based on the BLR, the 13 factors were considered the best factors affecting the elderly’s QoL at P < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity  =  0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly.

Conclusion

The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.

Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly

The elderly have an elevated risk of clinical depression because of isolation from family and friends and a reticence to report their emotional states. The present study explored whether data from a commercial neuroscience platform could predict low mood and low energy in members of a retirement community. Neurophysiologic data were collected continuously for three weeks at 1Hz and averaged into hourly and daily measures, while mood and energy were captured with self-reports. Two neurophysiologic measures averaged over a day predicted low mood and low energy with 68% and 75% accuracy. Principal components analysis showed that neurologic variables were statistically associated with mood and energy two days in advance. Applying machine learning to hourly data classified low mood and low energy with 99% and 98% accuracy. Two-day lagged hourly neurophysiologic data predicted low mood and low energy with 98% and 96% accuracy. This study demonstrates that continuous measurement of neurophysiologic variables may be an effective way to reduce the incidence of mood disorders in vulnerable people by identifying when interventions are needed.

Machine Learning on Early Diagnosis of Depression

To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.

Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges

The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.

Identifying patients with major depressive disorder based on tryptophan hydroxylase-2 methylation using machine learning algorithms

OBJECTIVES

This study aimed to identify patients with major depressive disorder (MDD) by developing different machine learning (ML) models based on tryptophan hydroxylase-2 (TPH2) methylation and environmental stress.

METHODS

The data were collected from 291 patients with MDD and 100 healthy control participants: individual basic information, the Negative Life Events Scale (NLES) scores, the Childhood Trauma Questionnaire (CTQ) scores and the methylation level at 38 CpG sites in TPH2. Information gain was used to select critical input variables. Support vector machine (SVM), back propagation neural network (BPNN) and random forest (RF) algorithms were used to build recognition models, which were evaluated by the 10-fold cross-validation. SHapley Additive exPlanations (SHAP) method was used to evaluate features importance.

RESULTS

Gender, NLES scores, CTQ scores and 13 CpG sites in TPH2 gene were considered as predictors in the models. Three ML algorithms showed satisfactory performance in predicting MDD and the BPNN model indicated best prediction effects.

CONCLUSION

ML models with TPH2 methylation and environmental stress were identified to possess great performance in identifying patients with MDD, which provided precious experience for artificial intelligence to assist traditional diagnostic methods in the future.

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper's overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Effects of social robots on depressive symptoms in older adults: a scoping review

Purpose

This review scopes evidence on the use of social robots for older adults with depressive symptoms, in the scenario of smart cities, analyzing the age-related depression specificities, investigated contexts and intervention protocols' features.

Design/methodology/approach

Studies retrieved from two major databases were selected against inclusion and exclusion criteria. Studies were included if used social robots, included older adults over 60, and reported depressive symptoms measurements, with any type of research design. Papers not published in English, published as an abstract or study protocol, or not peer-reviewed were excluded.

Findings

28 relevant studies were included, in which PARO was the most used robot. Most studies included very older adults with neurocognitive disorders living in long-term care facilities. The intervention protocols were heterogeneous regarding the duration, session duration and frequency. Only 35.6% of the studies had a control group. Finally, only 32.1% of the studies showed a significant improvement in depression symptoms.

Originality/value

Despite the potential for using social robots in mental health interventions, in the scenario of smart cities, this review showed that their usefulness and effects in improving depressive symptoms in older adults have low internal and external validity. Future studies should consider factors as planning the intervention based on well-established supported therapies, characteristics and needs of the subjects, and the context in which the subjects are inserted.

A Machine Learning Approach to Predicting New-onset Depression in a Military Population

OBJECTIVE

Depression is one of the most common mental disorders in the United States in both civilian and military populations, but few prospective studies assess a wide range of predictors across multiple domains for new-onset (incident) depression in adulthood. Supervised machine learning methods can identify predictors of incident depression out of many different candidate variables, without some of the assumptions and constraints that underlie traditional regression analyses. The objectives of this study were to identify predictors of incident depression across 5 years of follow-up using machine learning, and to assess prediction accuracy of the algorithms.

METHODS

Data were from a cohort of Army National Guard members free of history of depression at baseline (n = 1951 men and 298 women), interviewed once per year for probable depression. Classification trees and random forests were constructed and cross-validated, using 84 candidate predictors from the baseline interviews.

RESULTS

Stressors and traumas such as emotional mistreatment and adverse childhood experiences, demographics such as being a parent or student, and military characteristics including paygrade and deployment location were predictive of probable depression. Cross-validated random forest algorithms were moderately accurate (68% for women and 73% for men).

CONCLUSIONS

Events and characteristics throughout the life course, both in and outside of deployment, predict incident depression in adulthood among military personnel. Although replication studies are needed, these results may help inform potential intervention targets to reduce depression incidence among military personnel. Future research should further refine and explore interactions between identified variables.

A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure

In the present study, a systematic effort has been made to predict the hemolytic potency of chemically modified peptides. All models have been trained, tested, and evaluated on a dataset that contains 583 modified hemolytic peptides and a balanced number of non-hemolytic peptides. Machine learning techniques have been used to build the classification models using an immense range of peptide features that include 2D, 3D descriptors, fingerprints, atom, and diatom compositions. Random Forest based model developed using fingerprints as an input feature achieved maximum accuracy of 78.33% with AUC of 0.86 on the main dataset and accuracy of 78.29% with AUC of 0.85 on the validation dataset. Models developed in this study have been incorporated in a web server “HemoPImod” to facilitate the scientific community (http://webs.iiitd.edu.in/raghava/hemopimod/).

Unsupervised Classifications of Depression Levels Based on Machine Learning Algorithms Perform Well as Compared to Traditional Norm-Based Classifications

Large-scale screening for depression has been using norms developed based on a given population at a given time. Researchers have attempted to adjust the cutoff scores over time and for different populations, but such efforts are too few and far in between to be sensitive to temporal and regional variations. In this study, we proposed an unsupervised machine learning approach to constructing depression classifications to overcome the limitations of the traditional norm-based method. Data were collected from 8,063 Chinese middle and high school students. Using k-means clustering, we generated four levels of depressive symptoms to match the norm-based classifications. We then evaluated the validity of the classifications by comparing them with the norm-based method (and its variations) in terms of their robustness, model performance (accuracy, AUC, and sensitivity), and convergent construct validity (i.e., associations with known correlates). The results showed that our automatic classification system performed well as compared to the norm-based method.

Recommender System for Responsive Engagement of Senior Adults in Daily Activities

Understanding and predicting how people change their behavior after an intervention from time series data is an important task for health recommender systems. This task is especially challenging when the time series data is frequently sampled. In this paper, we develop and propose a novel recommender system that aims to promote physical activeness in elderly people. The main novelty of our recommender system is that it learns how senior adults with different lifestyle change their activeness after a digital health intervention from minute-by-minute fitness data in an automated way. We trained the system and validated the recommendations using data from senior adults. We demonstrated that the low-level information contained in time series data is an important predictor of behavior change. The insights generated by our recommender system could help senior adults to engage more in daily activities.

Evolutionary sequential genetic search technique-based cancer classification using fuzzy rough nearest neighbour classifier

Cancer is one of the deadly diseases of human life. The patient may likely to survive if the disease is diagnosed in its early stages. In this Letter, the authors propose a genetic search fuzzy rough (GSFR) feature selection algorithm, which is hybridised using the evolutionary sequential genetic search technique and fuzzy rough set to select features. The genetic operator's selection, crossover and mutation are applied to generate the subset of features from dataset. The generated subset is subjected to the evaluation with the modified dependency function of the fuzzy rough set using positive and boundary regions, which act as a fitness function. The generation and evaluation of the subset of features continue until the best subset is arrived at to develop the classification model. Selected features are applied to the different classifiers, from the classifiers fuzzy-rough nearest neighbour (FRNN) classifier, which outperforms in terms of classification accuracy and computation time. Hence, the FRNN is applied for performance analysis of existing feature selection algorithms against the proposed GSFR feature selection algorithm. The result generated from the proposed GSFR feature selection algorithm proved to be precise when compared to other feature selection algorithms.