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Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention [Hardback]

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  • Formāts: Hardback, 315 pages, height x width: 254x178 mm, weight: 825 g
  • Izdošanas datums: 26-Oct-2018
  • Izdevniecība: IGI Global
  • ISBN-10: 1522571310
  • ISBN-13: 9781522571315
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  • Cena: 318,71 €
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Pre-Screening Systems for Early Disease Prediction, Detection, and PreventionPalielināt
  • Formāts: Hardback, 315 pages, height x width: 254x178 mm, weight: 825 g
  • Izdošanas datums: 26-Oct-2018
  • Izdevniecība: IGI Global
  • ISBN-10: 1522571310
  • ISBN-13: 9781522571315
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781522571315.html
Keywords:
With the development of advanced screening procedures and techniques, certain limitations of the existing screening processes for disease methodologies and paradigms have been noted. More accurate and less invasive screening methods are needed to diagnose and treat health disorders and diseases before symptoms appear.

Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention is a pivotal reference source that utilizes advanced ICT techniques to solve problems in health data collection, analysis, and interpretation, as well as improve existing health systems for the advanced screening of diseases. Using non-invasive biomedical sensor devices and internet of things technology, this book examines safer methods to accelerate disease detection and effectively treat patients while challenging previously used pre-screening processes. While highlighting topics such as the applications of machine learning, patient safety, diagnostics models, and condition management, this publication is ideally designed for healthcare specialists, researchers in health informatics, industry practitioners, and academics.
Preface xv
Acknowledgment xxiv
Chapter 1 Internet-of-Things-Enabled Pre-Screening for Diseases: A Novel Approach for Improving the Conventional Methodology and Paradigm for Screening for Non-Communicable Diseases 1(49)
Thierry Edoh
This chapter focuses on the screening for non-communicable diseases (NCDs), which in certain cases are likely caused by infectious diseases.
The screening for NCDs in this specific case remains challenging since the convergence between both non-infectious and infectious diseases is less investigated.
This chapter, therefore, aims at reviewing and addressing the challenges and limitation of the conventional methodologies for screening for diseases, in general.
The chapter further proposes an innovative screening paradigm based on the internet of things technology.
The chapter presents the state of the art on the conventional screening for diseases, discusses the fundamental difference between screening for diseases and diseases surveillance and monitoring, and the difference between screening for diseases and diseases diagnostics.
Chapter 2 Barriers to Adoptions of IoT-Based Solutions for Disease Screening 50(19)
Sujitkumar Hiwale
Shrutin Ulman
Karthik Subbaraman
Change of disease patterns from communicable to chronic diseases has a tremendous impact on the healthcare ecosystem.
For healthcare organizations to remain viable and economically sustainable during this transition, there is a desperate need of cost-effective solutions for chronic disease management.
One important strategy for this is early diagnosis and management of diseases.
With rapid technological advancements, IoT-based solutions are well-positioned to be an effective tool for disease screening and health monitoring provided that they are also able to bridge non-technical barriers in technology adoption.
The three primary stakeholders for screening solutions are healthcare organizations, clinical fraternity, and end-users.
The primary objective of this chapter is to review likely barriers in adoptions of the IoT solutions from the perspective of these three primary stakeholders.
Chapter 3 Early Diagnostics Model for Dengue Disease Using Decision Tree-Based Approaches 69(19)
Shalini Gambhir
Yugal Kumar
Sanjay Malik
Geeta Yadav
Amita Malik
Classification schemes have been applied in the medical arena to explore patients' data and extract a predictive model.This model helps doctors to improve their prognosis, diagnosis, or treatment planning processes.The aim of this work is to utilize and compare different decision tree classifiers for early diagnosis of Dengue.
Six approaches, mainly J48 tree, random tree, REP tree, SOM, logistic regression, and naive Bayes, have been utilized to study real-world Dengue data collected from different hospitals in the Delhi, India region during 2015-2016.
Standard statistical metrics are used to assess the efficiency of the proposed Dengue disease diagnostic system, and the outcomes showed that REP tree is best among these classifiers with 82.7% efficient in supplying an exact diagnosis.
Chapter 4 Innovative Approaches for Pre-Screening and Sensing of Diseases 88(20)
Dharmpal Singh
Gopal Purkait
Abhishek Banerjee
Parag Chatterjee
Prescreening and sensing of diseases offers a number of benefits that can help in prevention of major diseases.
The aim of disease pre-screening is to detect possible disorders or diseases in people who do not have any symptoms.
Earlier screening methods for the detection of diseases was invasive, complicated, and would require extensive tests.
Some conventional methods used in clinical diagnoses include many invasive and potentially hazardous biopsy procedures, endoscopy, computed tomography; numerous innovative approaches have evolved to overcome the limitations of traditional techniques.
Non-invasive biomedical sensor, genomics, electronic nose, nano-material, plasmonicsensor devices, microfabrication-based technologies, flat-panel detectors, digital breast object models, endomicroscopy, breath biopsy, and wavelet-based enhancement methods are some of the emerging frontiers in prescreening and sensing of diseases.
This chapter will provide an in-depth discussion of the abovementioned innovative techniques related to prescreening and sensing of diseases.
Chapter 5 Clinical Decision Support System for Early Disease Detection and Management: Statistics-Based Early Disease Detection 108(48)
Likewin Thomas
Manoj Kumar M.V.
Annappa B.
Medical error is an adverse event of a failure in healthcare management, causing unintended injuries.
Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management.
CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression.
In this chapter, CDSS was developed with the help of statistical techniques.
Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN).
ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression.
A case progressing towards severity can be given better care, avoiding later stage complications.
Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN.
ModCNN showed better accuracy than other statistical techniques.
Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.
Chapter 6 Impact of Patient Health Education on the Screening for Disease Test- Outcomes: The Case of Using Educational Materials From the Internet and Online Health Communities 156
Thierry O.C. Edoh
Screening for diseases is a medical process to predict, prevent, detect, and cure a disease in people at high risk.
However, it is limited in the quality and accuracy of the outcomes.
The reason for this is the lack of long-term data about the health condition of the patient.
Launching modern information and communication technology in the screening process has shown promise of improving the screening outcomes.
A previous study has shown that patient education can positively impact the patient behavior face to a disease and can empower the patient to adopt a healthy lifestyle and thus avoid certain diseases.
Offering medical education to the patient can positively impact screening outcomes since educated and empowered patients are more aware of certain diseases and can collect significant information.
This can minimize the rate of false positive as well as false negative screening results.
This chapter analyzes how medical education can contribute to improving screening outcomes.