Integrated Deep Learning Model for Predictive Diagnosis of Life-Threatening Disease Condition

The degree of complexities associated with medical data has been always a rising concern among the healthcare sectors due to manifold valid reasons. Review of existing studies showcases usage of learning-based approaches being dominant towards solving diagnosis related issues of Life-Threatening Diseases or condition of patient during their hospital stay and yet characterized by various loopholes. Therefore, the proposed study addresses this problem by presenting a simplified and yet innovative integrated learning-based scheme towards leveraging predictive diagnosis performance on larger and complex medical dataset of patients in intensive care units. The scheme presents a Convolution Neural Network (CNN) based scheme integrated with Bidirectional Long Short-Term Memory (BiLSTM) with attention layer to find 54% increased predictive accuracy and 82% of faster response time in contrast to related method.