This seminar paper discusses how big data analytics might support healthcare organizations (e.g., hospitals) in medical diagnosis. The paper proceeds as follows: First, an overview of big data analytics in healthcare is provided with a focus on medical image analytics. Second, two large-scale image analysis cases are presented to materialize the theory upon which an integrated framework is proposed that illustrates how big data analytics might assist medical diagnosis. Third, the contemporary challenges of IT adoption in healthcare are discussed, and lastly, a brief conclusion is drawn.
Table of Contents
1. Introduction
2. Theoretical Background
2.1 Big Data Analytics in Healthcare
2.2 Image Analytics in Healthcare
3. Medical Diagnosis Cases and Proposed Framework
4. Discussion
5. Conclusion
Research Objectives and Themes
This paper aims to explore how big data analytics, specifically focusing on medical image analysis, can support healthcare organizations in clinical diagnosis, providing a theoretical framework to bridge the gap between large-scale data and effective decision-making.
- Application of big data analytics to improve medical diagnostic accuracy.
- Review of large-scale medical image analysis methodologies.
- Development of an integrated framework for data-driven clinical workflows.
- Discussion of challenges in IT adoption within the healthcare sector.
- Evaluation of the potential for personalized patient care through advanced analytics.
Excerpt from the Book
2.2 Image Analytics in Healthcare
According to Siuly and Zhang (2016), images are an important source for medical diagnosis, therapy assessment, and planning. Well-known imaging techniques are computed tomography (CT), X-ray, magnetic resonance imaging (MRI), and mammography. Generated images are shared using standard protocols like the digital image communication in medicine (DICOM) and stored in picture archiving and communication systems (PACS) (Luo et al., 2016). The data size of medical images can range from a few megabytes to hundreds of megabytes per study (Belle et al., 2015). The growing amount of medical images produced on a daily basis in modern hospitals requires a shift from traditional medical image analysis towards largely scalable solutions offering opportunities for greater use of computer-aided diagnosis (CAD) and decision support systems (Wang et al., 2018; Markonis et al., 2015). The volume, velocity, and variety of medical image data require large data storage capacity as well as fast and accurate algorithms.
Many prior studies have tested different methods for image analytics in healthcare (Siuly and Zhang, 2016). Among the classical machine learning methods for data mining, such as supervised learning (i.e. classification) and unsupervised learning (i.e. clustering), more advanced methods like support vector machines (SVM), neuronal networks, and artificial intelligence (AI) are often applied in this realm (Belle et al., 2015; Markonis et al., 2015; Dilsizian and Siegel, 2014). For example, classification and segmentation consist of assigning a label (e.g. healthy or diseased) to a given image, which is represented in a feature space that describes the image (e.g. color and texture). After having been trained, supervised machine learning algorithms are used to predict test image classes based on input visual features (Markonis et al., 2015).
Summary of Chapters
1. Introduction: This chapter introduces the increasing pressure on healthcare organizations to utilize big data analytics to improve patient care and clinical decision-making, while outlining the paper's focus on medical image analysis.
2. Theoretical Background: This section covers the fundamental concepts of big data (three V's) and its specific application to healthcare, alongside an overview of medical image analytics and machine learning techniques.
3. Medical Diagnosis Cases and Proposed Framework: This chapter presents specific case studies involving lung texture classification and traumatic brain injury, culminating in an integrated framework for medical diagnosis workflows.
4. Discussion: The discussion addresses the potential for big data to improve healthcare outcomes, while acknowledging the significant technical, legal, and economic challenges hindering current IT adoption.
5. Conclusion: The conclusion summarizes the potential of big data image analytics to assist clinical diagnosis and highlights the necessity for further quantitative research in this field.
Keywords
Big Data Analytics, Healthcare, Medical Diagnosis, Image Analytics, Machine Learning, Clinical Decision Support, Computer-Aided Diagnosis, Data Visualization, Hadoop, MapReduce, Electronic Health Records, IT Adoption, Artificial Intelligence, Personalized Patient Care, Medical Imaging.
Frequently Asked Questions
What is the core subject of this publication?
The paper explores the role of big data analytics in the healthcare sector, specifically focusing on how large-scale image analysis can support medical professionals in diagnosis and decision-making processes.
What are the primary thematic areas covered?
The main themes include the definition of big data in a healthcare context, medical image analytics, machine learning methodologies, challenges in IT infrastructure, and the creation of integrated clinical frameworks.
What is the overarching research goal?
The primary research question is: How might big data (medical image) analytics support healthcare organizations in clinical diagnosis?
Which scientific methods are analyzed in this paper?
The paper examines machine learning approaches, specifically supervised learning (classification) and unsupervised learning (clustering), as well as parallel computing frameworks like Apache Hadoop and MapReduce.
What topics are discussed in the main body of the text?
The main body focuses on existing literature on big data, detailed medical case studies utilizing CAD systems, and a proposed architectural framework for integrating medical images into the diagnostic workflow.
Which keywords best characterize this work?
Key terms include Big Data Analytics, Medical Diagnosis, Image Analytics, Artificial Intelligence, Clinical Decision Support, and IT Adoption in Healthcare.
How does the proposed framework improve upon existing processes?
The proposed framework helps healthcare organizations structure the interplay of people, processes, and technology, enabling more efficient and accurate diagnostic workflows compared to traditional siloed approaches.
What are the main barriers to implementing big data solutions in hospitals?
The paper identifies significant barriers, including data privacy regulations, the need for industry standards, vendor lock-in, data format variety, and the high cost of implementing sophisticated IT infrastructures.
Why is medical image data particularly challenging for big data analytics?
Medical images are high-volume, complex, and varied in nature, requiring significant storage capacity and fast, robust algorithms that can handle the nuance of organic shapes and textures across different imaging modalities.
- Citar trabajo
- Christian Marheine (Autor), 2018, Big Data Analytics in Healthcare to Assist Medical Diagnosis, Múnich, GRIN Verlag, https://www.grin.com/document/433121
