Abstract
Electronic Health Record (EHR) has gained significant importance in recent years due to its potential aspects in clinical prognosis and treatment. Patients’ clinical information is temporal and very crucial in predicting disease progression and proper care. However multivariate, mutually dependent, missingness nature of the data always bring new challenges and fresh experimental opportunities in the field. Traditional machine learning models primarily isolate and target the problems individually, incoherently. To ensure better and improved healthcare, computational models should be reasonable, modular, and generic over different clinical situations and setbacks. This thesis aims to develop advanced deep learning models for the purpose to predict the progressive disease path and find treatment policy to increase survivability for ICU patients. An improved multi-label deep sequential model synthesizes patients’ chronological critical conditions from dynamic temporal input. Subsequently, Multi-Agent Deep Reinforcement Learning (MARL) models have been developed to form a cohesive reward based environment to recommend dynamic treatment plans by scrutinizing the grid with feedback as well as generic clinical rules. Uncertainty is very common in analyzing patients’ condition. The study also encompasses sparse observations in ICU and assesses it as a learning component in Deep Reinforcement learning models through offline fashion. Conclusively, a complete healthcare encompasses both diagnosis and treatment. Consolidation of sequential deep learning and Reinforcement Learning models can help achieve a better AI assistive system for the physicians which can essentially improve a patient’s clinical procedures and offer better care.