Exploratory Data Analytics for Healthcare
- 298 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Exploratory Data Analytics for Healthcare
About This Book
Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way.
The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today.
The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.
Frequently asked questions
Information
1 Visual Analytics Scopes and Challenges
CONTENTS
- 1.1 Introduction: Concept of Visual Analytics
- 1.1.1 Process of Visual Analytics
- 1.1.2 Need and Benefits of VA in Healthcare
- 1.2 VA Technologies and Tools
- 1.2.1 General Features of Visual Analytics Tools
- 1.2.1.1 Data Visualization
- 1.2.1.2 Dashboards
- 1.2.1.3 Integration with Multiple Data Sources
- 1.2.1.4 Collaboration
- 1.2.2 Visual Analytics Tools
- 1.2.1 General Features of Visual Analytics Tools
- 1.3 Scope of VA in Different Sectors of Medical Science
- 1.4 Challenges to Face
- 1.5 Conclusion
- References
1.1 Introduction: Concept of Visual Analytics
1.1.1 Process of Visual Analytics
1.1.2 Need and Benefits of VA in Healthcare
- Using Electronic Health Record (EHR), a complicated set of structured and unstructured data that VA can use to quickly demonstrate the results in a sensible way for further course of action. The data may include a patientâs medical history maintained over time, including demographic data, development notes, complications, and treatments to provide need-based services.
- If technology needs future improvement in the healthcare system, then the EHR data used by doctors should be empowered by the emerging power of analytics and machine learning. Using modern analytical techniques, superior information can be provided to doctors for patient care. Meaningful inferences and predictions can be pulled out from it, like the probability of a patientâs successive risk to vital organ failure or catastrophic failure based on blood pressure reading, significant signs of illness, test results, genetic background, and recent clinical research data [2].
- Cloud computing infrastructure allows dynamic data processing with massive data. The extensive application of remote server accessibility in healthcare endorses policymakers to efficiently optimize operational management cost of treatment and offers individualistic care plans for better outcomes. It holds the potential to produce vast data compared to warehouses a decade ago [3].
1.2 VA Technologies and Tools
1.2.1 General Features of Visual Analytics Tools
1.2.1.1 Data Visualization
- Basic mathematics: understanding of linear equations, geometric procedures, and trigonometric function.
- Graphics: knowledge on canvas, vector-based graphics, Web Graphics Library, computer graphics, and network theory for creating or developing rich 2D and 3D interactive graphics.
- Engineering algorithms: arithmetical, basic, and general layout algorithms.
- Data analysis: scrutinizing, cleaning, transmuting, and demonstrating data for inherent knowledge discovery, drawing conclusions, and reviewing operational strategies.
- Design aesthetics: designing ethics, visual and color judgement, collaboration, thoughts, etc.
- Visual basis: visual coding (visual programing languages such as R, Scala, and Python Programming Languages), visual analysis, and graphical interaction.
- Visualization solutions: timely decisions through an in-depth analysis of raw data that helps in visualization of common business scenarios.
1.2.1.2 Dashboards
- Clinical dashboards: This dashboard integrates correlated data into a single point of display or âdashboardâ to optimize worker efficiency, accelerate medical judgements, rationalize workflow, and minimize human error in clinical practice. Here, clinical data are regularly recovered, recorded, examined, and shared to intended users for better care of patients. This also helps in tracking public health eruptions due to any flu epidemic to plan employment needs effectively by displaying the current loadable capacity of patients for emergency rooms, bed occupancy rates, nursing unit tallies, etc. [5].
- Hospital dashboards: It assists hospitals to keep a close and vigilant eye on significant functions of all departments for workflow and patient care quality improvements. The main objective of this dashboard is to follow patient satisfaction, optimum doctor staff-to-patient ratio, optimize the cost of care, and take care of required logistics. For this, it maintains a KPI structure that conducts surveys on the following parameters: Patient Satisfaction, Operational Workload, Operation Costs, Patient Volume, and Patient Experience. This includes surveys like patients admission, average waiting time, doctorsâ treatment plan, confidence in treatment, the average cost per patient and satisfaction, cost incurred by the department for patientsâ care, patients per doctor, number of doctors, etc. [6].
- Patient dashboards: This healthcare dashboard displays key information relating to patientsâ overall health. With this health summary dashboard, viewers can observe the following information: patientsâ vital signs, i.e., heart rate, temperature, and blood pressure, patients sleeping duration and the quality of sleep, daily step goals. It also helps to follow the aggregate duration of patient stay and laboratory waiting hours. Further, it may help physicians decide where to focus their treatment and preventive care efforts [7].
- Physician dashboards: This assists medical professionals to follow patient satisfaction, patient count, and new patient registration updates as well as track the metrics like the volume of cases treated, probable mortality, and reporting of adverse events, along with quality and process like significant parameters. To provide on-time treatment, appropriate appointment setup, lesser waiting time, increase patient safety, and reduce patient dissatisfaction, it is important to analyse healthcare plans for physicianâs allocation. By improving the ratio of patients per physician to 100:1, healthcare institutions can deliver better care by streamlining physician allocation through this dashboard.
- Quality and risk management dashboards: It supports in keeping a close eye on complex compliance issues, managing patient welfare information, hospitals recommended treatment count, and comparing hospital standards with in-house performance [8].
1.2.1.3 Integration with Multiple Data Sources
1.2.1.4 Collaboration
- Enhances confidence in data quality, reliability, and balance: collaboration increases the sample size; sharing helps in enhancing data quality. Inferencing the pattern of a particular case from a large data sample gives a more accurate prediction than using a small data sample. If a large scale of data is indicating the same issue, naturally, it enhances the dependability on data to achieve more accuracy in analysis.
- Enables better healthcare products: Accurate analysis from massive data gives better product development. It assists de...
Table of contents
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Editors
- Contributors
- Chapter 1 Visual Analytics: Scopes and Challenges
- Chapter 2 Statistical Methods and Applications: A Comprehensive Reference for the Healthcare Industry
- Chapter 3 Machine Learning Algorithms for Healthcare Data Analytics
- Chapter 4 A Review of Challenges and Opportunities in Machine Learning for Healthcare
- Chapter 5 Digitalizing the Health Records Using Machine Learning Algorithms
- Chapter 6 Interactive Visualization for Understanding and Analyzing Medical Data
- Chapter 7 Heart Disease Prediction Using Tableau
- Chapter 8 A Deep Learning Framework Using AlexNet for Early Detection of Pancreatic Cancer
- Chapter 9 Applications of the Map-Reduce Programming Framework to Clinical Big Data Analysis: Current Landscape and Future Trends
- Chapter 10 An Investigation of Different Machine Learning Approaches for Healthcare Analytics
- Chapter 11 The Potential of Machine Learning for Clinical Predictive Analytics
- Chapter 12 Predictive Analytics in Healthcare Using Machine Learning Tools and Techniques
- Chapter 13 A Collective Study of Machine Learning ( ML) Algorithms and Its Impact on Various Facets of Healthcare
- Index