MSCA ELITE-S Fellow
Trinity College Dublin
I have been working as a postdoctoral researcher at Trinity College, Dublin since June 15th 2022, after being selected jointly by Marie Sklodowska Curie Actions (MSCA) and ELITE-S ADAPT Centre Trinity College. My research project is ‘Personalisation of Relapse Risk in Autoimmune Disease: PARADISE study’ under the supervision of Professor (Dr) Mark Little (A clinician scientist with a research focus on rare autoimmune disease) and Dr James Ng (A statistician based in the school of Computer Science and Statistics in TCD)
I had been working as an Assistant Professor and later as Associate Professor in the Dept. of Computer Science at EMEA College of Arts and Science, Kondotty Kerala, India since 2005. I started my career as a system administrative trainee in the Department of Supercomputer Education and Research Centre(SERC) of Indian Institute of Science(IISC) , Bangalore, India in 2005.
I did M.Phil. in Data Mining in Bharathiar University, Coimbatore, India in 2008 and I was awarded Ph.D. by Kannur University, India in Data Mining in 2017. I have been serving as a reviewer in different international journals including Medical and Biological Engineering and Computing (MBEC , Springer Nature), IEEE Journal of Biomedical and Health Informatics (Formerly known as IEEE Transactions on Information Technology in Biomedicine) and IEEE Software etc.
‘Personalisation of Relapse Risk in Autoimmune Disease: PARADISE study’
Normally, the immune system protects our body against foreign invaders such as bacteria, fungi and viruses. When it senses these, the immune system sends out an army of fighter cells to attack foreign invaders. An autoimmune disease is a condition in which the immune system mistakenly attacks healthy cells in our body by wrongly considering its own cells as foreign. I will combine semantic web technologies, unique clinical datasets, targeted biomarker integration and patient-sourced health data to deliver a novel and ground-breaking solution to the prediction of flare in autoimmune disease. In parallel with this, I will link this initiative with the ongoing ISO/IEC JTC 1/SC42 standard concerned with artificial intelligence insofar as it applies to rare diseases.
‘Personalisation of Relapse Risk in Autoimmune Disease: PARADISE study’ aims to develop a robust, evidence-based standardised predictive model to predict ANCA-Associated Vasculitis (AAV) flare risk. Our approach uses semantic web technology to integrate standardised clinical data derived from a longitudinal inception cohort, physician clinical assessment, focused biomarker analysis and app-based patient feedback. The focus of this project is the development of novel Association Rule Mining based predictive models suitable for the complex longitudinal nature of the data and to embed the resulting algorithms alongside international standards development such as ISO/IEC JTC 1/SC 42 for Artificial Intelligence. Unlike many black box data mining and machine learning methods, humans can easily understand the decision-making process of ARM models. The interpretability of ARM based tools makes them particularly suitable to be deployed in a wide range of clinical environments by aligning them with international standards for health applications. The benefit of the project is potentially significant – healthcare costs in chronic inflammatory and autoimmune diseases can be expected to drop, while patient/carer burden, clinical time and resources will all be reduced.