Jul 2026· International Journal of Innovative Science and Research Technology· pp. 2027· 0 citations· 10 references
TL;DR
An AI-driven Depression Level Prediction System was created to collect structured clinical information, as well as unstructured textual input in order to create a full and complete assessment of an individualʼs mental health condition.
Abstract
AI is significantly impacting the way that mental health diagnostic tools are developed through their ability to
provide affordable, accessible and efficient means of detecting psychological disorders like depression. While the current
state of screening includes many effective tools (i.e., Clinical Interviews and Self- Reported Questionnaires) they have some
inherent shortcomings; these include, but are not limited to, being subjective and/or delayed diagnoses, lack of access to
individuals who may be experiencing difficulties with their mental health, and dependency on the availability of
professional interventions. The shortfalls identified above demonstrate the need for the development of intelligent systems,
capable of conducting rapid and accurate evaluations of an individualʼs mental health. An AI-driven Depression Level
Prediction System was therefore created to collect structured clinical information, as well as unstructured textual input in
order to create a full and complete assessment of an individualʼs mental health condition. Utilizing the PHQ 9 survey
instrument as the basis for collecting clinical information, the system utilizes Natural Language Processing techniques to
evaluate user-generated text, thereby gaining further insight into an individualʼs emotional and psychological trends.
The system described herein utilizes three machine learning-based predictive models Random Forest, SVM,
XGBoost) to predict an individualʼs level of depression as one of four categories (minimal, mild, moderate or severe).
Unlike prior binary prediction models utilized in the context of mental health evaluations, the described model
provides fine-tuned evaluations that can be more effectively used in practical applications of mental health
monitoring. Additionally, Explainable AI techniques were incorporated into the design of the system to improve
transparency and interpretability of the results produced by the system. Such capabilities enable both patients and
clinicians to identify specific variables within the results that contributed to the systemʼs predictions. The modular nature
of the system enables scalability, flexibility and efficient operation of the system even when utilizing lightweight
hardware that does not require extensive computing capabilities. Experimental validation demonstrated that the described
system achieved greater accuracy and better generalization than other systems currently available. Through its ability to
process both behavioral inputs, questionnaire responses and textual sentiment analysis, the system offers a holistic view
of an individualʼs mental health status. Beyond improving early detection, the described system can assist clinicians and
patients in making informed decisions regarding treatment options for issues related to mental health. Therefore, the
system serves as a connection between traditional healthcare practices and emerging AI technology to provide a private
and secure method for evaluating mental health conditions.
These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.
Juster Donal Sinaga· Journal of Society Counselin...· 0 citations
Mental health diseases such as Depression, Anxiety, Post-Traumatic Stress Disorder (PTSD), attention deficit hyperactivity disorder (ADHD), bipolar disorder, etc., have become a major health issue worldwide. Limited access to clinical services and delayed diagnosis remain a challenge for early detection of many disorders. The fast expansion of social media platforms has led to large libraries of textual data generated by users, which can provide useful insight into psychological well-being. We propose a novel machine learning-driven predictive computational model based on DistilBERT for automated multi-class mental health identification from Reddit posts in this paper. The proposed framework comprises data preparation, tokenization, feature extraction, transformer-based contextual encoding and supervised classification. The experimental evaluation was performed on the Reddit Mental Health Dataset, which contains 13,727 samples and 2,746 testing samples. The proposed model has obtained an accuracy of 95.6%, a precision of 94.7%, a recall of 96.1% and an F1-score of 95.8%. Our results indicate that transformer-based models are robust and scalable solutions for real-world mental health testing and decision-support systems.
Radhey Shyam, Prashant Yadav, Pawan Thakur· International journal on eme...· 0 citations
The rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.
Sayumi Nugaliyadde, Nawodya Nikeshi, M. Marasinghe et al.· 2026 4th International Confe...· 0 citations
Mental health disorders such as depression, anxiety, and post-traumatic stress disorder (PTSD) affect over one billion
people worldwide, yet early detection remains a major clinical challenge. In recent years, text data from social media posts,
clinical notes, and patient surveys has emerged as a rich source of signals for automated mental health screening. However,
most existing machine learning models operate as black boxes, limiting clinical adoption. This paper presents an interpretable
machine learning framework that combines natural language processing (NLP) feature extraction with explainable AI
techniques — specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic
Explanations) — to predict mental health conditions from text while providing transparent, clinically meaningful
explanations. A multi- class classification task involving depression, anxiety, PTSD, and healthy controls is performed on a
dataset of 19,320 labelled text samples. The proposed XGBoost model with SHAP explanations achieves 87.3% accuracy and
an AUC of 0.924, while the fine-tuned BERT model achieves 91.6% accuracy and an AUC of 0.961. Experimental results
demonstrate that interpretability does not significantly compromise predictive performance, enabling trustworthy AI-assisted
mental health screening.
N. Thakur, D. Patil, Pushpa Choudhary· International Journal for Re...· 0 citations
Artificial intelligence is being considered as a means of assisting psychiatric diagnosis, risk prediction, and long-term monitoring. Such applications in psychiatry have clinical relevance due to the fact that most psychiatric diagnosis is still based on interview, observation, and self-report, and also because it relies on symptom-based categorization that suffers from overlapping symptoms, tardiness in detection of illness, and discrepancies among patients given a single diagnosis. In this narrative review, we discuss applications of artificial intelligence in psychiatric assessment including machine learning, deep learning, natural language processing, digital phenotyping, large language models, and multimodal modelling. Papers published predominantly from 2020-2025 were included, with preference given to systematic reviews and meta-analyses, multicenter trials, and clinically significant publications.
It is evident that various computational models can detect valuable signatures in data obtained from electronic health records, neuroimaging, speech, clinical text, smartphone usage, wearable sensors, and social media. Such techniques can aid in the identification of depression, schizophrenia, bipolar disorder, anxiety disorders, and suicide risk. Multimodal approaches are particularly interesting due to the fact that they take the biological, psychological and social aspects of mental illness into consideration. On the other hand, the domain is currently suffering from insufficient and non-representative data sets, over-fitting, poor external validation, low interpretability, privacy issues, algorithmic bias, and unclear regulations. These aspects suggest that AI should be viewed as a supportive tool and not a replacement for the clinician.
Unknown authors· International Journal of Inn...· 0 citations
Schizophrenia is a severe psychiatric disorder marked by disturbances in thought, perception, and behavior, resulting in long-term functional impairment and a substantial societal burden. Early identification is essential, as delayed diagnosis is highly associated with poorer prognosis, increased relapse risk, and prolonged untreated illness. Conventional diagnostic practice depends largely on clinical interviews and behavioral assessment, which are subjective, resource intensive, and limited in identifying early or prodromal stages. This research presents a structured review of recent AI-driven approaches for schizophrenia detection, encompassing machine learning, deep learning, and hybrid learning strategies. These methods have been applied to a wide range of data sources, including biological markers, behavioral traits, physiological recordings and medical imaging data. The reviewed studies report consistently high discriminatory performance across multiple data sources, demonstrating the potential to complement traditional clinical evaluation. However, common limitations were observed, including small and homogeneous cohorts, absence of longitudinal follow-up, sensitivity to preprocessing choices, limited transparency, and weak cross-site validation. Variability in data acquisition protocols and inconsistent reporting of clinical factors such as medication status and illness duration further restrict reproducibility and clinical translation. Additionally, many studies focus only on binary classification, ignoring symptom severity, disorder subtypes, and relapse patterns that are important in clinical practice. The absence of common benchmarks and shared evaluation protocols also limits fair comparison and wider adoption. By consolidating current findings and highlighting unresolved challenges, this work outlines key directions for developing reliable, interpretable, and scalable systems suitable for real clinical settings.
Deepa V. V., S. Diwakaran· 2026 7th International Confe...· 0 citations
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