Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability.

Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries.

Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.

Socio-demography and medical history as predictors of health-related quality of life of breast cancer survivors.

BACKGROUND: Even after completion of conventional treatment, breast cancer survivors continue to exhibit a variety of psychological and physical symptoms, affecting their quality of life. The study aimed to investigate the relationship between socio-demography, medical characteristics and health-related quality of life (HR-QOL) of a sample of breast cancer survivors in Malaysia. MATERIALS AND METHODS: This pilot cross-sectional survey was conducted among breast cancer survivors (n=40) who were members of Breast Cancer Support Group Centre Johor Bahru. A validated self-administered questionnaire was used to identify the relationships between socio-demography, medical characteristics and HR-QOL of the participants. RESULTS: Living with family and completion of treatment were significant predictive factors of self-rated QOL, while living with family and ever giving birth significantly predicted satisfaction with health and physical health. Psychological health had moderate correlations with number of children and early cancer stage. Survivors' higher personal income (>MYR4,500) was the only significant predictor of social relationship, while age, income more than MYR4,500 and giving birth significantly predicted environment domain score. CONCLUSIONS: The findings suggested the survivors coped better in all four HR-QOL domains if they were married, lived with family, had children and were employed.