Publications by authors named "Soman Elangovan"
Article Synopsis
- * Recent research is investigating the use of Machine Learning, particularly deep learning techniques, to predict which patients are likely to benefit from rTMS, utilizing functional MRI data to identify responsive versus non-responsive patients.
- * Experiments show that this new model significantly outperforms traditional methods in predicting treatment outcomes, achieving high accuracy rates and identifying key brain connectivity measures that influence rTMS response.
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.
View Article and Find Full Text PDFBackground: 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.