Dr. AI: A Heterogeneous Clinical Decision Support System for Personalised Health Care

Doctors and health workers are the key front-liners fighting relentlessly to save human lives from various diseases and hidden enemies, such as germs and microbes, since the dawn of civilisation. However, extreme work pressure due to colossal number of patient with finger-countable health workers and doctors makes it hard to maintain the quality of their service. Medical science is a vast, incredibly complicated, and ever-growing field that make the task of health workers harder and urge them to remain updated always. Hence, an AI assistant to suggest possible diagnosis and treatment options can ease the life of doctors to some extent and keep them updated. Existing clinical decision support systems (CDSSs) are either knowledge-based or pattern-based, and very few of them are for personalised care. In this paper, we propose and develop (a prototype version of) a heterogeneous CDSS using pattern mining, machine learning, and knowledge-based techniques. Finally, we conclude the paper with scopes for future extension of the proposed system.