The prime purpose of the proposed study is to construct a novel predictive scheme for assisting in the prognosis of criticality using the MIMIC-III dataset. With the adoption of various analytics and advanced computing in the healthcare system, there is an increasing trend toward developing an effective prognostication mechanism. Predictive-based modeling is the best alternative to work in this direction. This paper discusses various scientific contributions using desk research methodology towards the Medical Information Mart for Intensive Care (MIMIC-III). This open-access dataset is meant to help predict patient trajectories for various purposes ranging from mortality forecasting to treatment planning. With a dominant machine learning approach in this perspective, there is a need to discover the effectiveness of existing predictive methods. The resultant outcome of this paper offers an inclusive discussion about various available predictive schemes and clinical diagnoses using MIMIC-III in order to contribute toward better information associated with its strengths and weaknesses. Therefore, the paper provides a clear visualization of existing schemes for clinical diagnosis using a systematic review approach.

Various predictive frameworks have evolved over the last decade to facilitate the efficient diagnosis of critical diseases in the healthcare sector. Some have been commercialized, while others are still in the research and development stage. An effective early predictive principle must provide more accurate outcomes in complex clinical data and various challenging environments. The open-source database system medical information mart for intensive care (MIMIC) simplifies all of the attributes required in predictive analysis in this regard. This database contains clinical and non-clinical information on a patient’s stay at a healthcare facility, gathered during their duration of stay. Regardless of the number of focused research attempts employing the MIMIC III database, a simplified and cost-effective computational technique for developing the early analysis of critical problems has not yet been found. As a result, the proposed study provides a novel and cost-effective machine learning framework that evolves into a novel feature engineering methodology using the MIMIC III dataset. The core idea is to forecast the risk associated with a patient’s clinical outcome. The proposed study focused on the diagnosis and clinical procedures and found distinct variants of independent predictors from the MIMIC III database and ICD-9 code. The proposed logic is scripted in Python, and the outcomes of three common machine learning schemes, namely Artificial Neural Networks, K-Nearest Neighbors, and Logistic Regression, have been evaluated. Artificial Neural Networks outperform alternative machine learning techniques when accuracy is taken into account as the primary performance parameter over the MIMIC III dataset.

The ZUC stream cipher belongs to synchronous stream cipher category. The cipher is word oriented and produces two words (32-bits) cipher text per cycle. The ZUC uses 128 bits key and 128-bits Initialization Vector (IV) for its operation. The research work considers FPGA implementation of pipelined ZUC architecture. The presented work uses compact architecture for modulo adders (over 2 ³¹ -1) used in feedback path and modulo adders (Over 2 ³² ) used in Finite State Machine (FSM). It also uses novel S-box architectures to minimize hardware resources. The presented architecture coded using VHDL language using Xilinx ISE 14.2 software for synthesis and simulation. The hardware implementation of the design uses Xilinx FPGA Virtex-5 XC5VFX130T having package FF1738. The presented hardware attained throughput of 5411.8 Mbps at clock frequency of 169.119MHz. The result shows the considerable hardware optimization.

The contribution of machine learning towards prediction of critical state of patient is the prime focus of the current study. The review of current approaches of machine learning has been witnessed with various shortcomings. Hence, the proposed study adopts medical information mart for intensive care (MIMIC-IV) dataset in order to develop a novel analytical model that can predict the criticality state of patient in their next visit. The model has been designed by hybridizing convolution neural network (CNN) and long short-term memory (LSTM) which takes the discrete input of hospital and individual patient information in each visit. The concatenated feature is then subjected to a newly introduced recommender module which offers implicit feedback by assigning a ranking score. The final predictive outcome of study offers criticality rank. The study model is benchmarked with existing machine learning approaches to find 54% of increased accuracy and 70% of reduced processing time.

Machine learning has proven to be one of the efficient solutions for analyzing complex data to perform identification and classification. With a large number of learning tools and techniques, the health section has significantly benefited from solving the diagnosis problems. This paper has reviewed some of the recent scientific implementations on learning-based schemes to find that existing studies of learning have mainly focused on predictive analysis with less emphasis on preprocessing and more inclination towards adopting sophisticated learning schemes that offer higher accuracy at the cost of the higher computational burden. Therefore, the proposed method addresses the concern mentioned above by a novel computational learning model that emphasizes fine-tuning complex medical data and makes it suitable for learning to balance better classification performance and computational complexity. The implementation is carried out using the MIMIC-III dataset, where the proposed system discretizes, the complete model using physician reports and furnished patient information as the first step. It also prepares the data by choosing a specific tuple and its associated field. The second step introduces a novel relatedness function where preprocessing is carried out using word quantization while adopting auto-encoders in deep learning followed by a novel learning-based diagnosis. The outcome exhibits that the proposed system offers better classification performance in reduced processing time in comparison to existing learning schemes.