Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR)

In world, patients suffering from chronic and lifestyle diseases are substantially increasing that effects social as well as economic life. In this work, initially a broad survey of ubiquitous, smart and networked healthcare systems for monitoring of patients with chronic and lifestyle diseases is presented. Afterwards, Smart Patient Monitoring and Recommendation, a novel framework based on Deep Learning (DL) and Cloud oriented analytics is proposed. Based on the patients' vital signs and activity context, generated through Ambient Assisted Living devices, SPMR monitors and predicts the real health status and calls assistive services. The real time processing and intelligence facilitated by both Local Intelligent Processing (LIP) module and cloud oriented analytics devised in SPMR. LIP is based on predictive DL with novel Categorical Cross Entropy (CCE) Optimization. In the experimental study, imbalanced dataset collected through a case study on patients suffering from Chronic Blood Pressure disorder is utilized and real health status of patient is predicted. SPMR offers prevention and care in real time even in the absence of internet and cloud service. It eliminates the drawbacks of existing works, in which Machine Learning models and associated methods are copied to local portion. Our proposed model demonstrates the efficacy when compared with similar and recent models. The highest accuracy improvement with our model ranges from 8-18%. Also, F-score average and F-score for emergency class improved up to 17% and 36% respectively. The results show the effectiveness of SPMR even in case of emergencies.