Big Data are highly effective for systematically extracting and analyzing massive data. It can be useful to manage data proficiently over the conventional data handling approaches. Recently, several schemes have been developed for handling big datasets with several features. At the same time, feature selection (FS) methodologies intend to eliminate repetitive, noisy, and unwanted features that degrade the classifier results. Since conventional methods have failed to attain scalability under massive data, the design of new Big Data classification models is essential. In this aspect, this study focuses on the design of metaheuristic optimization based on big data classification in a MapReduce (MOBDC-MR) environment. The MOBDC-MR technique aims to choose optimal features and effectively classify big data. In addition, the MOBDC-MR technique involves the design of a binary pigeon optimization algorithm (BPOA)-based FS technique to reduce the complexity and increase the accuracy. Beetle antenna search (BAS) with long short-term memory (LSTM) model is employed for big data classification. The presented MOBDC-MR technique has been realized on Hadoop with the MapReduce programming model. The effective performance of the MOBDC-MR technique was validated using a benchmark dataset and the results were investigated under several measures. The MOBDC-MR technique demonstrated promising performance over the other existing techniques under different dimensions.

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Background The radiology report is the way of communication between the radiologists and the clinicians of different specialties. Each part of the report is important and significant in the patient management plan. Therefore, knowledge of interpretation and behavior in understanding the final report is a variable crucial skill. Methods This is a cross-sectional survey study to explore the behavior and attitude of clinicians toward radiology reports in relation to their professional clinical demographic. A total of 107 physicians participated, including consultants, specialists, and residents among different specialties. Results Among the 107 responses, 58.9% were male and 41.1% were female. The majority of the physicians (78.5%) read the radiology report for every requested study for each patient, while 21.5% of participants didn't read the radiology report for the studies they requested, instead, they only read it occasionally. Gender played a significant factor, as female practitioners were more likely to read the complete radiology report (P = 0.033). In addition, the age of the practitioner was also significant as clinicians in the age group 40-60 years old were more likely to check the requested radiology image prior to reading the report compared to age groups 20-39 and >60 years (P = 0.035). Lastly, specialists were significantly more likely to read the entire radiology report compared to consultants and residents (P = 0.006). Conclusion More emphasis and awareness should be provided to clinicians on the importance of reading the entire radiology report as some information can be missed if not being read completely.

Given the high caseload most radiology departments face on a daily basis, workflow optimization becomes a necessity to avoid delays and poor health outcomes. This requires detailed analysis of workflow data to identify problem areas in the process. Analysis of the clinical imaging process demands an understanding of temporal intervals and temporal event sequences and relationships. Working with radiology staff, we seek to provide a tool to help monitor and improve radiology department workflow in order to increase efficiency and productivity and ensure the delivery of timely clinical imaging reports. In this thesis, I present RadStream: a web-based retrospective, exploratory, interactive data visualization tool that provides a comprehensive overview of the radiology department's daily activities. I worked closely with radiology staff to analyze the department workflow and classify the analytical tasks required by domain experts in order to inform the design of the tool. Together, we abstracted the steps involved in the clinical imaging process. We also identified factors affecting the workflow (such as personnel, machine availability, and resources) and noted the relationship between the different factors as it plays an important role in increasing productivity. RadStream depicts the steps involved in the process of clinical imaging and shows the flow of processes from one step to the next. The visual representation emphasizes the time intervals between the different steps and uses colour coding to denote the status of a process (on time, acceptably late, late) in compliance with standard radiology turnaround times (TATs). The main focus of RadStream is on monitoring performance with special attention to duration, delays, and compliance with standard TATs. RadStream was evaluated by radiology staff using hospital data and real scenarios to evaluate its effectiveness, efficiency, and usability. The initial feedback received was very promising. And based on results collected from the evaluation studies, I sensed a general acceptance and excitement about the system as a quality assurance tool. I have also collected some constructive feedback to build upon for future releases. Finally, I reflect on lessons l learned from iteratively designing RadStream, and present design guidelines for the design of visual analytics tools for health care.