Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e68030 - e68030

Опубликована: Апрель 30, 2025

Perinatal depression and anxiety significantly impact maternal infant health, potentially leading to severe outcomes like preterm birth suicide. Aboriginal women, despite their resilience, face elevated risks due the long-term effects of colonization cultural disruption. The Baby Coming You Ready (BCYR) model care, centered on a digitized, holistic, strengths-based assessment, was co-designed address these challenges. successful BCYR pilot demonstrated its ability replace traditional risk-based screens. However, some health professionals still overrely psychological risk scores, often overlooking contextual circumstances mothers, strengths, mitigating protective factors. This highlights need for new tools improve clinical decision-making. We explored different explainable artificial intelligence (XAI)-powered machine learning techniques developing culturally informed, predictive modeling perinatal distress among mothers. identifies evaluates influential factors while offering transparent explanations AI-driven decisions. used deidentified data from 293 mothers who participated in program between September 2021 June 2023 at 6 care services Perth regional Western Australia. original dataset includes variables spanning factors, life events, worries, relationships, childhood experiences, family domestic violence, substance use. After applying feature selection expert input, 20 were chosen as predictors. Kessler-5 scale an indicator distress. Several models, including random forest (RF), CatBoost (CB), light gradient-boosting (LightGBM), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector (SVM), (EBM), developed compared performance. To make black-box interpretable, post hoc explanation Shapley additive local interpretable model-agnostic applied. EBM outperformed other models (accuracy=0.849, 95% CI 0.8170-0.8814; F1-score=0.771, 0.7169-0.8245; area under curve=0.821, 0.7829-0.8593) followed by RF (accuracy=0.829, 0.7960-0.8617; F1-score=0.736, 0.6859-0.7851; curve=0.795, 0.7581-0.8318). Explanations EBM, explanations, identified consistent patterns key questions related "Feeling Lonely," "Blaming Herself," "Makes Family Proud," "Life Not Worth Living," "Managing Day-to-Day." At individual level, where responses are highly personal, XAI provided case-specific insights through visual representations, distinguishing illustrating predictions. study shows potential XAI-driven predict provide clear, human-interpretable how important interact influence outcomes. These may help more non-biased decisions mental screenings.

Язык: Английский

---

Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder

Journal of Affective Disorders, Год журнала: 2025, Номер 378, С. 281 - 292

Опубликована: Март 3, 2025

Язык: Английский

---

A Dual-Channel Prediction-Interpretation Framework with Pre-Trained Language Models and SHAP Explainability

Journal of Computer and Communications, Год журнала: 2025, Номер 13(03), С. 116 - 137

Опубликована: Янв. 1, 2025

Язык: Английский

---

Integrating AI-Powered Personalization via Neurobiologically Informed DBT in Smart Wellness Resorts

Springer proceedings in business and economics, Год журнала: 2025, Номер unknown, С. 923 - 953

Опубликована: Янв. 1, 2025

Язык: Английский

---

Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment

Diagnostics, Год журнала: 2024, Номер 14(21), С. 2385 - 2385

Опубликована: Окт. 25, 2024

Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed detect depression assess severity, aiming improve diagnostic precision provide insights into contributing factors.

Язык: Английский

---

Machine Learning with Explainability for Suicide Ideation Detection from Social Media Data

Опубликована: Окт. 30, 2023

Suicide is one of the major causes death globally. Analysis social media posts and in-depth insights show that some people have suicide ideas. In order to save more lives, it crucial comprehend behavior suicidal attempters. However, identifying explaining thoughts poses a significant challenge in psychiatry. Additionally, analysing complex procedure involving several variables based on individual's preferences data type. Although traditional methods been utilized identify clinical factors for ideation detection (SID), these models often lack interpretability understanding. Therefore, primary aim this research apply deep learning (DL) machine (ML) techniques such as BERT, LSTM, BiLSTM, RF, SVM, GaussianNB, LR, KNeighbors blending with interpretable LIME SHAP provide valuable into importance different features make transparent SID process. The experiments were conducted publicly available dataset comprising 24,101 posts, categorized either or non-suicidal. implemented method brings about enhancements performance comparison. A comparison all measures reveals LSTM model particularly good at processing classifying textual data, higher accuracy, precision, recall, AUC scores than other tested.

Язык: Английский

---

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Sensors, Год журнала: 2023, Номер 24(1), С. 164 - 164

Опубликована: Дек. 27, 2023

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental needs globally. Increasingly, passively acquired data from wearables augmented with carefully selected active depressed individuals develop Machine Learning (ML) models of depression based on mood scores. However, most ML black box in nature, hence outputs not explainable. Depression is also multimodal, reasons for may vary significantly individuals. Explainable personalised will thus be beneficial clinicians determine main features that lead decline state individual, enabling suitable therapy. This currently lacking. Therefore, this study presents methodology developing accurate Deep (DL)-based predictive depression, along novel methods identifying key facets exacerbation depressive symptoms. We illustrate our approach by an existing multimodal dataset containing longitudinal Ecological Momentary Assessments lifestyle neurocognitive assessments 14 mild moderately participants over one month. classification- regression-based DL predict participants’ current scores—a discrete score given participant severity their The trained inside eight different evolutionary-algorithm-based optimisation schemes optimise model parameters maximum performance. A five-fold cross-validation scheme used verify model’s performance against 10 classical ML-based models, error as low 6% some participants. use best process extract indicators, SHAP, ALE Anchors explainable AI literature explain why certain predictions made how they affect mood. These feature insights can assist professionals incorporating interventions into individual’s treatment regimen.

Язык: Английский

---

Decoding Perinatal Mental Health: Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health through Explainable Machine Learning

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 2, 2024

Background Perinatal mental health significantly affects mothers, infants, and families. Despite their resilience strengths, Aboriginal mothers experience disproportionate physical disparities. These result from historical ongoing impacts of colonization the resultant complex trauma. Conventional approaches to perinatal care present many barriers for who frequently feel disengaged, apprehensive unsafe. Current score-based risk-screening practices that algorithmically drive referrals, further ingrain fears including culturally biased judgments child removal. The Baby Coming You Ready (BCYR) model centred around a digitised, holistic, strengths-based assessment, was co-designed address these barriers. recent successful pilot demonstrated BCYR effectively replaced all current risk-based screens. However, professionals disproportionately rely on psychological risk scores, overlooking contextual circumstances cultural strengths mitigating protective factors. Methods To this singular reliance screening psychometrics whilst supporting strengthened considered clinical we propose sensitive eXplainable AI (XAI) solution. It combines XAI with lived experience, knowledge wisdom generate prediction support being screened. solution can identify, prioritise, weigh both maternal factors, quantify relative mental-health well-being at group individual levels. Results Different machine learning algorithms, Random Forest, K-nearest neighbour, vector machine, alongside glassbox Explainable Boosting Machine (EBM) models, were trained real life de-identified data generated during pilot. Additionally, techniques like SHAP LIME are utilised interpretability black box models. show EBM demonstrates superior performance in prediction, an accuracy 0.849, F1 score 0.771 AUC 0.821. Global explanations across entire dataset local cases, achieved through different methods, compared showed similar stable results. Conclusions This study potential enhance professionals' capability responsive reasoning improve strengthen outcomes women.

Язык: Английский

---

Deep Learning-Based Depression Analysis Among College Students Using Multi Modal Techniques

International Journal of Advanced Computer Science and Applications, Год журнала: 2024, Номер 15(7)

Опубликована: Янв. 1, 2024

This study proposed a novel approach to handle mental health, particularly, depression among college students, called CRADDS A Comprehensive Real-time Adaptive Depression Detection System. The combined advanced tensor fusion networks which is able analyze emotions using audio, text and video data more accurately, this possible due the strength of deep learning multimodal approaches. system constructed with hybrid algorithm framework that combines SVM (Support Vector Machines), CNN (Convolutional Neural Network) (Bidirectional Long-Term Short-Term Memory) BiLSTM techniques. To address limitations identified in earlier research, increasing its feature set effective machine algorithms reduce false positives negatives. Further, it includes IoT devices collect real time from various range public private sources. symptoms may be continuously monitored time, helps identify depressions early stages guaranteed perfect well-being students. Additionally, model has ability adjust based on interaction features, provide psychological support automatic responses observed verbal nonverbal clues. Experiments show obtained an impressive accuracy features text, audio video, when compared existing models. Overall, useful tool for health professionals educational institutions because not only identifies but also treat earlier, guarantees good academic scores general well-being. validation increases 63.04% 86.08% higher than model.

Язык: Английский

---

Artificial Intelligence Technologies in Mental Health

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 219 - 262

Опубликована: Дек. 23, 2024

This chapter explores the incorporation of artificial intelligence (AI) into mental health care, with a particular focus on managing depression. AI has significantly enhanced promotion, detection, diagnosis, treatment, and monitoring depression by leveraging technologies such as machine learning, natural language processing, wearable devices. also discusses various AI-driven approaches, including analysis questionnaires, medical records, social media, speech data, electroencephalogram, magnetic resonance imaging, chatbots, virtual reality, face analysis, robots, multimodal methods, Each these offers unique benefits, increased accuracy in detecting depression, personalized treatment plans, continuous patient monitoring. However, challenges linked to health, data privacy issues, biases algorithms, complexity human emotions. The concludes highlighting opportunities future research directions innovation for enhancing care.

Язык: Английский

---