Big Data Analytics and Intelligence : A Perspective for Health Care.

Intro -- Half Title Page -- Title Page -- Copyright Page -- Contents -- About the Editors -- About the Authors -- Preface -- Chapter 1-Big Data Analytics and Intelligence: A Perspective for Health Care -- 1. Introduction -- 2. Big Data Overview -- 3. Big Data Applications in Health Care -- 3.1.1. Levels of Staffing. Staffing levels are set by administrators of the particular organization and these factors are influenced by various forces such as budgetary considerations and features of local nurse labor markets. The administrative departmen -- 3.1.2. Outcomes. Capturing and analyzing the patient information helps in generating a summarized report so that it can be used in later stages for better understanding. Even though it has resulted in great success still this method is very challenging be -- 3.1.3. Conclusion. A difference can be seen in healthcare sections where staffing is less when compared to institutions where staffing is more. Most of the researches that were conducted suggest that if nurses appointed are less than required it creates u -- 3.2. Electronic Health Records. Needs and Advantages -- 3.2.1. Introduction. The most important task of EHR is to help in understanding the medical background of patients with the help of electronic mechanism rather than using traditional techniques of maintaining papers or folders. This helps in reducing time -- 3.2.2. Importance for Improving Efficiency and Productivity. One of the main aims of maintaining EHR is that it helps to retrieve information's regarding the patients whenever required. Lab results can be gathered from decades ago with less amount of time -- 3.2.3. Application. The application of EHRs ranges from government sectors to financial sections of various industries. Few of the applications and the expected outcomes from the particular industries are as follows.

3.2.4. Aggregated Data. Since decisions are to be taken based on the past experiences, most of the organizations collect high quality data in raw format. These data are mainly procured from the data collected from inpatient and outpatient data and details -- 3.2.5. Integrated Data. The main disadvantage of maintaining paper-based health records in that it can be used to combine other paper health records and store as the same. Since this mechanism lacks the ability to integrate with other paper forms of infor -- 3.2.6. Conclusion. In order to modernize the infrastructure in healthcare sector it is required to adopt and implement EHRs-based systems. A survey conducted to understand the importance of EHR shows that it helps to identify patients with serious health -- 3.3. Enhancing Patient Engagement -- 3.3.1. Introduction. The healthcare industry like any other sector of the society works mainly to gain profit and survive in the business field. Since patients are the most important factor in the healthcare institutions it is important to ensure they are -- 3.3.2. Patient-reported Outcomes. Most of the people who invest in a healthcare sector are mainly interested in improving and expanding the existing business. There are few techniques that are followed to increase the profit. One of the techniques is by c -- 3.3.3. Values of Patient-reported Outcomes. By identifying the needs of the stakeholders and other staffs in a healthcare organization has helped in understanding the importance of report-based mechanism. Most of the surveys conducted on report-based deci -- 3.3.4. Conclusion. The development of technology and mechanisms used for treatments have helped in getting much better and appropriate accurate way of solving diseases. It has also helped in identifying methods by which health conditions can be improved w.

3.4. Big Data to Understand Cure for Cancer -- 3.4.1. Introduction. In the healthcare-related industry, the concept of big data was introduced recently. Big data analytics has helped in taking many positive measures to improve the procedures involved with patient care. It allows a more efficient depen -- 3.5. Predictive Analytics in Health Care -- 3.5.1. Introduction. The main aim of predictive analytics in health care is to help various organizations and healthcare sectors to discover the data and convert it into information that can be used to improve business decisions (Al Mamoon et al., 2013). -- 3.5.2. Application of Predictive Analytics. Critical care intervention provides surveillance type of mechanism for solving and making alerts so that it will help in reducing risk faced by patients from infections and unwanted drug allergies. -- 3.5.3. Text Mining Medical Records. With the increase in the use of EHRs has led to a situation which has forced to adopt data-mining techniques to understand the data retrieved from various health reports. The content of the Electronic Health Record (HER -- 3.5.4. Conclusion. The decision support systems mainly search for large percentage of unstructured text and use them for decision-making after it is analyzed and made meaningful. These data after analyzing are stored in databases so that it can be used in -- 3.6. Need for Security and a Mechanism to Reduce Fraud in Big Data -- 3.6.1. Introduction. There is a huge need for big data in health care as well, due to rising costs in countries like the India. Studies show that there will be an increase in the demand for implementing big data analytics tool for improving the healthcare -- 3.6.2. Three Levels of Security. The security tools need to be implemented at three various levels that are not present in the network. These levels are as follows:.

3.6.3. Challenges in Securing Big Data. Few of the challenges that are faced in securing big data are as follows: -- 3.6.4. Conclusion. From various studies and research conducted it enables to understand that the end-users are mostly responsible for data after it is being converted into useful information. Most of the users of big data implements security measures but -- 3.7. Telemedicine -- 3.7.1. Introduction. Telemedicine is the process that includes giving and taking data in return regarding various drugs from one site to another through electronic medium. The main aim of telemedicine is to help in improving the health condition of the pa -- 3.7.2. Application of Telemedicine. The origin of telemedicine mainly started by providing assistance through communication-based technology. Most of the research based on the history of telemedicine indicates that it was first used for delivering prescri -- 3.7.3. Conclusion. As days passes the risk involved with various diseases are increasing and alarming. Technological devices are being invented to avoid contact directly with the patients and these devices are being updated day by day. This technological -- 3.8. Applications of Big Data -- References -- Chapter 2-Big Data Analytics in Health Sector: Need, Opportunities, Challenges, and Future Prospects -- Introduction -- Big Data -- BD Definitions in the Health Sector -- BD Needs in the Health Sector -- The Health Care Analytics Environment -- EHRs -- EMRs -- Sensor Data -- Internet of Things -- BDA Techniques, Tools, and Technologies in Health Sector -- Opportunities in Health through BDA Use -- Challenges and Strategies -- Few Strategies to Overcome the Challenges of BDA in the Health Sector -- Conclusion and Prospects -- References -- Chapter 3-Use of Classification Algorithms in Health Care -- Introduction -- Data Mining in Health care.

Classification Algorithms used in the Healthcare Industry -- Determining the Value of K. In the KNNs algorithm, finding the optimal value of K is of utmost importance as it influences the result and accuracy obtained on the data to a great extent. The k-value in KNN is referred to as the instances in the training s -- Naïve Bayes Algorithm -- Understanding the Working of a Naïve Bayes Classifier. The basic assumption made while building a Naïve Bayes Classifier is that each attribute is independent of the other in the prediction of the output variable. Moreover, none of the attributes make an -- Support Vector Machines -- Understanding the Concept of Hyperplanes. In simple terms, the data points in the training set of a particular dataset define a vector object termed as the hyperplane. Conventionally, the maximum margin hyperplane in SVMs is used in classification problem -- Decision Trees -- Entropy. For a particular finite set S, Shannon's Entropy is denoted as H(s). The uncertainty of the data is measured by entropy. Entropy can be defined by equation 7. -- Information Gain. The splitting of data is followed by a decrement in value of Entropy which decides the Information Gain. The information serving with the greatest value of information gain makes the best decision tree which means that the branches of th -- Gini Index. A population is said to be pure if two items which are selected randomly belong to the same probability and class. The basic working of a Gini Index is based on categorical variables, namely, True or False. The homogeneity in the data is said -- Chi-square -- Pruning -- Random Forest.

Training a Random Forest Classifier. The training of the random forest classifier takes place through the technique of bagging. A bagging technique is also termed as bootstrap aggregation which involves random sampling of the training set of data in such.

Big Data Analytics and Intelligenceis essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.