Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression.

BackgroundPsychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Language Processing (NLP) methodologies enable the automated extraction of informative speech features, which has been leveraged for early psychosis detection and assessment of symptomology. However, critical gaps persist, including the absence of standardized sample collection protocols, small sample sizes, and a lack of multi-illness classification, limiting clinical applicability. Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity.Methods:We adopted online data collection methods to collect 20 minutes of speech and demographic information from individuals. Participants were categorized as “healthy” help-seekers (HC), having a schizophrenia-spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), or being on the psychosis spectrum with sub-clinical psychotic experiences (SPE). SPE status was determined based on self-reported clinical diagnosis and responses to the PHQ-8 and PQ-16 screening questionnaires, while other diagnoses were determined based on self-report from participants. Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. A 70%-30% train-test split and 30-fold cross-validation was used to validate the model performance.Results:The final analysis sample included 1140 individuals and 22,650 minutes of speech. Using 5-minutes of speech, our model could discriminate between HC and those with a serious mental illness (SSD or BD) with 86% accuracy (AUC = 0.91, Recall = 0.7, Precision = 0.98). Furthermore, our model could discern among HC, SPE, BD and SSD groups with 86% accuracy (F1 macro = 0.855, Recall Macro = 0.86, Precision Macro = 0.86). Finally, in a 5-class discrimination task including individuals with MDD, our model had 76% accuracy (F1 macro = 0.757, Recall Macro = 0.758, Precision Macro = 0.766).Conclusion:Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.

Natural Language Processing (NLP) has emerged as a transformative technology within the domain of artificial intelligence, significantly advancing the ability of machines to comprehend and interact with human language. Recent advancements in machine learning algorithms have led to substantial improvements in a variety of NLP applications, including sentiment analysis, machine translation, automated summarization, and question answering. This study presents a novel approach to NLP, which integrates advanced machine learning techniques to enhance performance in these areas. The proposed method achieves a high level of accuracy, with an overall accuracy rate of 97.6%. Additionally, it demonstrates strong performance in error metrics, including a Mean Absolute Error (MAE) of 0.403 and a Root Mean Square Error (RMSE) of 0.203. These results indicate a significant advancement in the precision and effectiveness of language-based tasks. Despite these achievements, challenges such as the management of ambiguous language, the need for extensive annotated datasets, and the demand for real-time processing capabilities remain. This paper aims to explore the current state of NLP driven by machine learning, examining key techniques and methodologies that have propelled its development. The analysis will cover various machine learning approaches applied to NLP, evaluate their impact on different applications, and identify ongoing challenges and future research directions. By providing a comprehensive overview of the interplay between NLP and machine learning, this research seeks to enhance understanding of how these technologies can be leveraged to improve language processing and communication

Assessment center (AC) exercises such as role‐plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP‐based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent‐related validity and contamination with word count of ML scores based on models that used different NLP methods to operationalize verbal behavior. Second, for the model that maximized convergence while minimizing contamination with word count (i.e., a model that used both n‐grams and Universal Sentence Encoder embeddings as predictors), we investigated the criterion‐related validity of its scores. Third, we examined how the interrater reliability of the AC role‐play scores affects ML model convergence. To do so, we applied seven NLP methods to 96 assessees' transcriptions and trained 10 sets of ML models across 18 speeded AC role‐plays to automatically score assessee performance. Results suggest that ML scores recovered most of the original variance in the overall assessment ratings, and replacing one or more human assessors with ML scores maintained criterion‐related validity. Additionally, ML models seemed to exhibit higher convergence when assessors consistently detected and utilized observable behaviors to make ratings (i.e., when interrater reliability was higher). Finally, we provide a step‐by‐step guide for practitioners seeking to implement ML scoring in ACs.

The recall of traumatic memories is central to clinical and neurobiological models of PTSD, yet neurocircuitry mechanisms underlying traumatic memory recall remain elusive. Recent advances in natural language processing and large language models enable complex semantic quantification of autobiographical memories. Here, we leveraged these analytic approaches to define the neurocircuitry encoding the semantic content of traumatic autobiographical narratives among individuals with PTSD. 79 women with PTSD related to interpersonal violence listened to traumatic and neutral autobiographical narratives during fMRI. Sentence-level brain activity and semantic embeddings were quantified for each script and participant. Neurocircuitry encoding semantic content of the narratives was defined through cross-validation across participants. A priori regions of interest included the hippocampus, superior temporal gyrus (STG), and posterior cingulate cortex (PCC). Our approach detected significant hippocampal sensitivity for semantic content of both trauma and neutral narratives; however, spatial encoding patterns of semantic content within the hippocampus differed between trauma and neutral narratives. Specifically, spatial encoding patterns in CA1 and dentate gyrus differentiated narrative type. Regardless of narrative type, PTSD symptom severity was positively associated with semantic encoding across the hippocampus and its subfields, except for the subiculum. For trauma narratives, semantic sensitivity was greater within the left STG and decreased in the PCC and broader default mode network. Encoding in neither region tracked with PTSD symptom severity. These results reveal a hippocampal role in mediating recall of specific semantic content for traumatic and neutral autobiographical narratives and suggest hippocampal sensitivity to autobiographical semantic content underlies greater PTSD symptom severity. Clinical trial registration information: Improving Therapeutic Learning for PTSD, Study Details | NCT04558112 | Improving Therapeutic Learning for PTSD | ClinicalTrials.gov, NCT04558112.

Questions are presented at “consideration points” that follow a section that gives information about the case. One or more choices may be correct for each question; make your choices on the basis of your clinical knowledge and the history provided. Read all of the options for each question before making any selections. You are given points on a graded scale for the best possible answer(s), and points are deducted for answers that would result in a poor outcome or delay your arriving at the right answer. Answers that have little or no impact receive zero points. At the end of the exercise, you will add up your points to obtain a total score.

Malicious websites have become a pervasive concern in the digital realm, targeting careless users as well as organizations. It may result in substantial financial losses, identity theft, data intrusions, and damage to reputation. In order to create efficient countermeasures, it is crucial to comprehend the effects of interacting with such websites. There are several ways to classify malicious webpages. Web content analysis is one such way for protecting internet users from malicious activities. It entails analysing websites to identify potential hazards, such as phishing attempts, malware distribution, and fraudulent activities. Traditional methods relied on rule-based systems, but recent advances in natural language processing and machine learning have opened up new avenues for increasing the precision and scalability of web content analysis to classify malicious webpages. Most of the exciting research work focuses more on URL alone for risk free processing. This paper introduces novel method for analysing web contents especially textual contents of the webpages for classification. Among various tags in web technology, proposed method focuses on div, paragraph and meta tags. The textual contents of these tags are extracted and vectorized using three different vectorizers in natural language processing and classify the webpages using machine learning models. Seven different machine learning models are used for performance evaluation. The result shows that a combined textual content of three distinct tags with count vectorizer + random forest achieves the higher accuracy of 93.46% with 1000 features.

The current Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) specifies that generalized anxiety disorder (GAD) should not be diagnosed if it occurs exclusively during an episode of a major depressive disorder (MDD) or another mood disorder. This hierarchy rule was intended to promote diagnostic parsimony, but may result in the loss of important clinical information. The goal of this study was to compare individuals with MDD, comorbid MDD and GAD, and GAD within the course of MDD at intake and 12-month follow-up on self-report measures, clinician ratings, and rates of comorbidity. Participants were divided into three diagnostic groups: MDD without GAD (n=124), comorbid MDD and GAD (n=59), and GAD within the course of MDD (n=166). All the participants completed a semi-structured clinical interview and self-report measures assessing psychopathology, temperament, and functional impairment. A subset of the total sample completed a follow-up assessment of 12 months postintake. Individuals with comorbid MDD and GAD and GAD within the course of MDD reported more psychopathology, negative affect, and functional impairment at intake than individuals with MDD only. The presence of GAD at intake, however, did not differentially predict symptom severity, functional impairment, or the presence of comorbidity at 12-month follow-up. Cross-sectional findings indicate that individuals with GAD within the course of MDD experience levels of psychopathology, functional impairment, and comorbidity similar to those found in individuals with comorbid GAD and MDD. Preliminary longitudinal findings, however, suggest that the presence of GAD in patients with MDD does not have prognostic significance.

Detecting fake news is a critical challenge in natural language processing (NLP), demanding solutions that balance accuracy, interpretability, and computational efficiency. Despite advances in NLP, systematic empirical benchmarks that directly compare both classical and deep models—across varying input richness and with careful attention to interpretability and computational tradeoffs—remain underexplored. In this study, we systematically evaluate the mathematical foundations and empirical performance of five representative models for automated fake news classification: three classical machine learning algorithms (Logistic Regression, Random Forest, and Light Gradient Boosting Machine) and two state-of-the-art deep learning architectures (A Lite Bidirectional Encoder Representations from Transformers—ALBERT and Gated Recurrent Units—GRUs). Leveraging the large-scale WELFake dataset, we conduct rigorous experiments under both headline-only and headline-plus-content input scenarios, providing a comprehensive assessment of each model’s capability to capture linguistic, contextual, and semantic cues. We analyze each model’s optimization framework, decision boundaries, and feature importance mechanisms, highlighting the empirical tradeoffs between representational capacity, generalization, and interpretability. Our results show that transformer-based models, especially ALBERT, achieve state-of-the-art performance (macro F1 up to 0.99) with rich context, while classical ensembles remain viable for constrained settings. These findings directly inform practical fake news detection.

In the ever-changing realm of E-Commerce, it is essential for online businesses to comprehend and adjust to shifting consumer behaviour in order to achieve long-term success. In which, Intelligent Recommendation System (IRS) has gained familiarity by suggesting personalized information based on user preference and behaviours. Hence, the review paper primarily aims to analyse significance of the intelligent recommendation system to transform ecommerce field, specifically enrich the user personalisation and satisfaction, and enhance revenue in business. Accordingly, the proposed survey is discussed the traditional system and AI-powered personalization system in ecommerce. AI-powered recommendation system utilize sophisticated algorithms to analyse extensive data, allowing for the provision of highly customized and relevant content, product recommendation, and user satisfaction. Besides, it examines future trends in AI integration within e-commerce, particularly advancements in Natural Language Processing (NLP) and visual search technologies, which are poised to further enrich ecommerce. The paper concludes with a look toward future directions for the integration of AI technologies in e-commerce, anticipating advancements in NLP and visual search capabilities, which promise to further enhance the online shopping experience. Overall, the findings of the article underscores the transformative impact of IRS on the e-commerce sector, advocating for their continued development in response to evolving market demands.

IntroductionThe Contrast Avoidance Model (CAM) was developed to explain pathological worry in generalized anxiety disorder (GAD). The CAM posits that those with GAD are sensitive to sharp increases in emotions, and use worry to maintain heightened states of negative arousal to avoid these emotional shifts. Research has widely supported the CAM in the conceptualization of GAD, and has extended these findings to other disorders, including major depressive disorder (MDD) and social anxiety disorder (SAD). Despite the utility of the CAM model in informing the etiology of these conditions, research has yet to expand these findings beyond GAD, MDD, and SAD. Specifically, obsessive-compulsive disorder (OCD), which co-occurs with GAD, MDD, and SAD in adults at a rate of 15.0%, 40.7%, and 14.7%, respectively, and shares many of their etiological features, has yet to examined in the context of the CAM. Thus, examining CA as a relevant mechanism and therapeutic target for OCD is an unstudied conceptual framework that may offer meaningful clinical utility.ObjectivesThe present study used receiver operator curve (ROC) analyses to examine the predictive utility of the CAQ-W and CAQ-GE in detecting probable OCD in a large undergraduate sample. We hypothesized that the CAQ-W and CAQ-GE would be higher in participants with probable OCD and would offer sufficient sensitivity and specificity in predicting probable OCD.Methods1259 undergraduates were recruited for a mass University screening. Participants were included in the OCD group (N = 291) if they met diagnostic criteria for OCD (DOCS total score > 20). Participants were included in the nondisordered group (n = 249) if they did not meet diagnostic criteria for any of the screened disorders (SAD, MDD, GAD, OCD, panic disorder, post-traumatic stress disorder, OCD, borderline personality disorder), denied suicidality, and denied receiving mental health treatment in the last 12 months. ROC analyses were used to examine the accuracy of the CAQ-W and the CAQ-GE in detecting probable OCD.ResultsResults of ROC analyses are reported in Table 1. AUC values for the CAQ-W and CAQ-GE were significantly different from the null hypothesis (AUC = .50, p < .001), and demonstrated excellent (.89) to outstanding (.91) accuracy in predicting probable OCD (Figure 1), respectively. Optimal sensitivity and specificity to detect probable OCD (Table 2) was achieved at a cut off score of 67.5 for the CAQ-W (Sensitivity = 81.4%; Specificity = 82.3%) and a cutoff score of 43.5 for the CAQ-GE (Sensitivity = 84.9%; Specificity = 85.5%).Image:Image 2:Image 3:ConclusionsResults suggest that OCD can be accurately characterized by CA. Findings also highlight the utility of examining CA as a relevant maintenance factor for OCD symptoms. Future research should examine the impact of CA on OCD symptoms in-laboratory and ecological settings.Disclosure of InterestNone Declared

Natural Language Processing (NLP) is an essential element of computational linguistics and artificial ‎intelligence, enabling fluid interactions between humans and machines. Social networking networks ‎produce substantial volumes of user-generated text daily, offering both opportunities and challenges for ‎NLP researchers. Social media discourse's informal, dynamic, and context-dependent characteristics ‎necessitate specific NLP techniques for precise processing and analysis. This study thoroughly examines NLP applications in social media, including essential tasks such as sentiment analysis, topic ‎modeling, misinformation detection, and hate speech identification. It examines the influence of machine ‎learning and deep learning methodologies, particularly transformer models, on the advancement of NLP ‎capabilities. This study also emphasizes the ethical issues related to NLP-driven social media apps, ‎including data privacy, algorithmic bias, and the regulation of misinformation. The paper continues by ‎discussing emerging research paths, highlighting the necessity for adaptable and ethical NLP solutions in ‎the changing social media environment‎.

Task-oriented dialogue systems employ third-party APIs to serve end-users via natural language interactions. while existing advances in Natural Language Processing (NLP) and Machine Learning (ML) techniques have produced promising and useful results to recognize user intents, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose a new approach to recognize and realize complex user intents. Our approach relies on a new rule-based technique that leverages both (i) natural language features extracted using existing NLP and ML techniques and (ii) contextual knowledge to capture the different classes of complex intents. We devise a context knowledge service to capture the requisite contextual knowledge.KeywordsTask-oriented conversational botsComplex intent recognitionContext knowledgeSlot value inference

Accumulating evidence shows that nitro-oxidative pathways play an important role in the pathophysiology of major depressive disorder (MDD) and bipolar disorder (BD) and maybe anxiety disorders. The current study aims to examine superoxide dismutase (SOD1), catalase, lipid hydroperoxides (LOOH), nitric oxide metabolites (NOx), advanced oxidation protein products (AOPP), malondialdehyde (MDA), glutathione (GSH), paraoxonase 1 (PON1), high-density lipoprotein cholesterol (HDL), and uric acid (UA) in participants with and without generalized anxiety disorder (GAD) co-occurring or not with BD, MDD, or tobacco use disorder. Z unit-weighted composite scores were computed as indices of nitro-oxidative stress driving lipid and protein oxidation. SOD1, LOOH, NOx, and uric acid were significantly higher and HDL and PON1 significantly lower in participants with GAD than in those without GAD. GAD was more adequately predicted by increased SOD + LOOH + NOx and lowered HDL + PON1 composite scores. Composite scores of nitro-oxidative stress coupled with aldehyde and AOPP production were significantly increased in participants with comorbid GAD + MDD as compared with all other study groups, namely MDD, GAD + BD, BD, GAD, and healthy controls. In conclusion, GAD is characterized by increased nitro-oxidative stress and lipid peroxidation and lowered lipid-associated antioxidant defenses, while increased uric acid levels in GAD may protect against aldehyde production and protein oxidation. This study suggests that increased nitro-oxidative stress and especially increased SOD1 activity, NO production, and lipid peroxidation as well as lowered HDL-cholesterol and PON1 activity could be novel drug targets for GAD especially when comorbid with MDD.

Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, revolutionizing various fields including voice assistants and chatbots. This paper explores the latest developments in NLP technologies and their applications in enhancing the functionality and intelligence of voice assistants and chatbots. Beginning with an overview of NLP and its core components, such as tokenization, syntactic analysis, semantic understanding, and machine learning techniques, this paper delves into the cutting-edge techniques driving the evolution of NLP. Furthermore, it discusses the challenges faced by NLP systems and the strategies employed to address them. Subsequently, the paper explores the utilization of NLP in voice assistants and chatbots, elucidating how these technologies are reshaping human-computer interaction, improving user experiences, and expanding the scope of applications in various domains. Additionally, it examines the impact of NLP advancements on the future trajectory of voice assistants and chatbots, highlighting potential opportunities and implications for research and industry

As customer expectations continue to evolve, businesses are increasingly turning to artificial intelligence (AI) to reshape how they deliver service and support. This review explores how AI technologies such as chatbots and virtual assistants are playing a central role in streamlining customer interactions and offering faster, more personalized experiences. This review paper gives an insight of the evolution of the chatbots and virtual assistants, along with comparison between different chatbots available in market. This paper also explores the Indian market size which is being controlled by these tools and their future trend .These tools rely on advances in natural language processing (NLP), machine learning (ML), and speech recognition so as to mimic human conversation and enhance user satisfaction. The paper outlines the advantages of using AI in customer service, including round-the-clock availability, improved efficiency, and scalable service delivery. It also critically examines limitations, such as the absence of emotional intelligence, difficulty with complex queries, and the potential risks of over-reliance on automation. Emerging developments like multilingual and emotion-aware bots, AR integration, and the future influence of quantum computing are explored.The review concludes by suggesting that AI should be adopted responsibly, with a balanced approach that combines human judgment with the speed and efficiency of technology.