AI method that finds a hyperplane using support vectors and margins to separating the tuples of two classes.
Published in Chapter:
Challenges and Opportunities of Soft Computing Tools in Health Care Delivery
André S. Fialho (Massachusetts Institute of Technology, USA), Federico Cismondi (Massachusetts Institute of Technology, USA), Susana M. Vieira (Technical University of Lisbon, Portugal), Shane R. Reti (Harvard University, USA), João M. C. Sousa (Technical University of Lisbon, Portugal), and Stan N. Finkelstein (Massachusetts Institute of Technology, USA)
Copyright: © 2013
|Pages: 20
DOI: 10.4018/978-1-4666-3990-4.ch016
Abstract
During the last decade, modern hospitals have witnessed a growth in the amount of information acquired, stored, and retrieved more than ever before. While aimed at helping healthcare personnel in providing care to patients, this high stream of data can also have a negative impact if not delivered in a simple and organized way. In this chapter, the authors explore the current opportunities and challenges that soft computing predictive tools face in healthcare delivery, and they then present an example of how some of these tools may contribute to the decision-making of health care providers for an important critical condition in Intensive Care Units (ICU)—septic shock. Despite current challenges, such as the availability of clean clinical data, accuracy, and interpretability, these systems will likely act to enhance the performance of a human expert and permit healthcare resources to be used more efficiently while maintaining or improving outcomes.