An Industrial IoT Approach for Pharmaceutical Industry Growth

Volume 2

Valentina Emilia Balas, Vijender Kumar Solanki, Raghvendra Kumar, Valentina E. Balas

• Publisher: Academic Press
• Year: 2020
• Print ISBN: 9780128213261
• eBook ISBN: 9780128213278
• Topic: Medicine
• Subtopic: Pharmaceutical, Biotechnology & Healthcare Industry

An Industrial IoT Approach for Pharmaceutical Industry Growth, Volume Two uses an innovative approach to explore how the Internet of Things (IoT) and big data can improve approaches and make discoveries. Rapid growth of the IoT has encouraged many companies in the manufacturing sector to make use of this technology to unlock its potential. Using clear language and real-world case studies, this book discusses systems level from both a human-factors point-of-view and the perspective of networking, databases, privacy and anti-spoofing. The wide variety in topics presented offers multiple perspectives on how to integrate the Internet of Things into pharmaceutical manufacturing.This book represents a useful resource for researchers in pharmaceutical sciences, information and communication technologies, and those who specialize in healthcare and pharmacovigilance. - Emphasizes efficiency in pharmaceutical manufacturing through an IoT/Big Data approach    - Explores cutting-edge technologies through sensor enabled environments in the pharmaceutical industry - Discusses system levels from both a human-factors point-of-view and the perspective of networking, databases, privacy and anti-spoofing

Chapter 1

Medical big data mining and processing in e-health care

A. Vidhyalakshmi¹ and C. Priya², ¹Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India, ²Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India

Abstract

Health care is thought to be one of the business fields with the largest big data potential. Based on the prevailing definition, big data has a large amount of data which can be processed easily and can be modified or updated easily. These data can be quickly stored, processed, and transformed into valuable information using older technologies. At present, many new trends regarding new data resources and innovative data analysis are followed in medicine and health care. In practice, electronic health-care records, free open-source data, and the “quantified self” provide new approaches for analyzing data. Some of these advancements have been made in information extraction from the text data based on analytics, which is useful in data unlocking for analytics purposes from clinical documentation. Choosing big data approaches in the medicine and health-care fields has been lagging. This has led to the rise specific problems regarding data complexity and organizational, legal, and ethical challenges. With the growth of the uptake of big data in general, and medicine and health care in specific, innovative ideas and solutions are expected. Telemedicine is a new opportunity for the Internet of Things (IoT). This enables the specialist to consult a patient despite them being in different places. Medical image segmentation is needed for the analysis, storage, and protection of medical images in telemedicine. Telemedicine is defined by the World Health Organization (WHO) as “the practice of medical care using interactive audiovisual and data communications. This includes the delivery of medical care services, diagnosis, consultation, treatment, as well as health education and the transfer of medical data.” IoT-based applications mainly include remote patient monitoring and clinical monitoring. In addition, preventive measures-based applications are also part of smart health care. These applications require image processing-based technologies which could be integrated into medical health-care systems. Various types of input taken from cameras and processing of CT and MRI images could be integrated into IoT-based medical applications.

Keywords

Big data; IoT; health care; telemedicine; WHO; image processing

1.1 Introduction

In any industry big data can be changed by managing, analyzing, and leveraging the data. Health care is a promising area in which this could be applied to create a change in the field, which would result in many advantages. In health care, quality of life is improved by reducing treatment costs, epidemic outbreak prediction, and avoiding preventable diseases. The global population is increasing leading to new challenges for treatments and delivery methods. Health professionals are similar to business entrepreneurs, with a massive volume of data being obtained by them which could be selected and used by using the best strategies.

Any information related to human death, health conditions, quality of life, and reproductive outcomes is referred to as health data, whether for an individual or a population. Health data consist of clinical metrics along with environmental, socioeconomic, and health and wellness-based behavior information. The communication of individuals with health-care systems is collected as health data for processing and use by health-care providers. These data consist of a record of received services, service conditions of those data, and practical outcomes obtained in the clinic or information concerning those services [1]. By having this kind of framework most health data can be sourced. The advances in health information technology (IT) and eHealth care have been enhanced by the use of health data. The advancements in health care also enhance data security, data privacy, and ethical concerns. The main and major components of digital health are achieved by increasing data collection and usage.

Health data are split into two types: structured and unstructured. When the data are easily transferred between the health information system and in standardized form then the system is said to be structured [2]. The name of the patient, patient DOB, and output of a blood test report are stored in a structured data format whereas nonstandardized data are considered as unstructured health data [2]. Examples of unstructured data include emails, audio recordings, or physician notes about a patient. Based on the collection and use of health data, the information system is expanded, in a health-care field the data complexity is reduced with standardization [3]. In 2013, it was observed that 60% of data in the United States was unstructured [4].

The industrial internet is defined as the human services on the IoT, these terms suggest about expanding number of brilliant very quickly, then the gadgets will be moved among the connection between the gadgets and the amount of information and individuals. Some assessments have estimated that $120 billion was spent in 4 years for medicinal services in the IoT cloud, with human services IoT being the major part of this information in an unstructured format. This unstructured data information working within the Hadoop system has made Hadoop an interesting part of the progress into investigating a wide range of information. Recently, miscellaneous collections of screen gadgets are used, from glucose screens to fetal screens, fetal screens to electrocardiograms, and then to circulatory strains for each patient. Thereafter a subsequent visit from a doctor would be important with this huge amount of estimations.

When intelligent medical devices are able to interpret the results of other devices and gadgets, the requirements for direct doctor action may be replaced by a telephone call to the patient from a medical attendant. In addition patients can use devices at home and have the data transferred electronically. The possible results provided by human services IoT could result in immense improvements to patient care. In this human services substructure, utilization of the IoT can enable the installation of hardware, programming, sensors, and systems to permit these devices to communicate with one another and trade information.

The IoT has many possible cutting-edge innovations which could affect the whole range of businesses including health care. In today’s framework this can be considered as the interrelation of exceptionally identifiable items and gadgets with advantages and benefits which can be achieved by going beyond the machine-to-machine environment.

1.1.1 Types of big data

The definition and types of big data and are discussed next.

1.1.1.1 Structured

Data which can be stored, processed, and retrieved in a fixed form are known as structured data. These contain well-ordered information which can be promptly and perfectly stored. These data can be acquired from a database using search engine techniques. For example, the “workers” table in a firm’s database will be designed to include details of the employees, their positions, salaries, etc., in a well-structured manner.

1.1.1.2 Unstructured

Unstructured data are data that are unavailable in any specific form or structure. These kinds of data are complex to process and the processing time for these types of data can be excessive. Email is an example of unstructured data.

1.1.1.3 Semistructured

Data which contains both structured and unstructured formats is defined as semistructured data. Specifically this indicates information which has not been put under a particular database, and which consists of tags or vital information that separates individual elements of the data.

1.1.2 Characteristics of big data

1.1.2.1 Variety

Big data that are collected from many of multiple sources may be structured, unstructured, and/or semistructured. Previously, data may have been gathered from Excel sheets and databases, however nowadays the data are also sent in the forms emails, PDFs, photos, videos, audios, SM posts, and etc.

1.1.2.2 Velocity

Velocity is defined as the rate of speed at which data are generated in real time. In a broader context, it incorporates the rate of change, interconnection of incoming data sets at various speeds, and activity bursts.

1.1.2.3 Volume

The word big data has its meaning in the words themselves. Big data is purely a large amount of data that is being created on a day-by-day basis from various resources such as human interactions, social media platforms, machines, business processes, and networks. Such a large volume of information is saved in a data repository.

1.1.3 Integration of big data with medical imaging

Medical imaging and its processing plays a vital role in medicine in the United States, where about 600 million imaging procedures are performed annually. It is difficult manually to examine and store these images and also expensive and time consuming as hospitals need to protect and store them for several years in case of the need for future analysis by radiologists.

Medical imaging distributor care streams illustrate how often images were changed while analyzing the big data in health. The algorithms developed by physicians should analyze specific patterns in the hundreds of thousands of pixels in images and convert them into a numerical format for diagnosis. Furthermore, it could be feasible that radiologists in the future will no longer need to look at the images, but instead algorithms would analyze the outcomes as they are able to produce and remember a greater number of images. This will clearly affect the work of radiologists and their education and skills.

1.1.4 Advantages of health-care data management

The advantages of using health-care data management include the following:

• • Producing 360 degree views of consumers, patients, and households, and deploying personalized, guided conversations by associating data from all available sources.
• • Enhanced patient engagement with predictive modeling and analysis based on health-care data.
• • Improved population health outcomes in specific geographic areas by tracking current health trends and predicting upcoming ones.
• • Ma...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• About the editors
• Preface
• About the book
• Chapter 1. Medical big data mining and processing in e-health care
• Chapter 2. Brain–computer interfaces and their applications
• Chapter 3. Transforming pharma logistics with the Internet of things
• Chapter 4. Drug identification and interaction checking using the Internet of Things
• Chapter 5. Accelerating data acquisition process in the pharmaceutical industry using Internet of Things
• Chapter 6. Internet of Things applications in the pharmaceutical industry
• Chapter 7. Internet of Things: from hype to reality
• Chapter 8. The Internet of Things: looking beyond the hype
• Chapter 9. Internet of Things: the new Rx for pharmaceutical manufacturing and supply chains
• Chapter 10. Smart cervical band: an Internet of Things- and artificial intelligence-based neck pain and cervical spondylosis healing system
• Chapter 11. Medical data analysis in eHealth care for industry perspectives: applications
• Index