This paper discusses possible motivations to adopt cognitive computing- based solutions in the field of healthcare and surveys some recent experiences. From a very practical point of view, the use of cognitive computing techniques can provide machines with human-like reasoning capabilities, thus allowing them to face heavy uncertainties and to cope with problems whose solution may require computing intensive tasks. Moreover, empowered by reliable networking infrastructures and cloud environments, cognitive computing enables effective machine-learning techniques, resulting in the ability to find solutions on the basis of past experience, taking advantage from both errors and successful ndings. Owing to these special features, it is perceptible that healthcare can greatly bene t from such a powerful technology. In fact, clinical diagnoses are frequently based on statistics and signi cant research advancements were accomplished through the recursive analysis of huge quantity of unstructured data such as in the case of X-ray images or computerized axial tomography scans. As another example, let us consider the problem of DNA sequence classi cation with the uncountable combinations that derive from such a complex structure.

ADOPTING COGNITIVE COMPUTING SOLUTIONS IN HEALTHCARE

Paolo Maresca
Software
2018

Abstract

This paper discusses possible motivations to adopt cognitive computing- based solutions in the field of healthcare and surveys some recent experiences. From a very practical point of view, the use of cognitive computing techniques can provide machines with human-like reasoning capabilities, thus allowing them to face heavy uncertainties and to cope with problems whose solution may require computing intensive tasks. Moreover, empowered by reliable networking infrastructures and cloud environments, cognitive computing enables effective machine-learning techniques, resulting in the ability to find solutions on the basis of past experience, taking advantage from both errors and successful ndings. Owing to these special features, it is perceptible that healthcare can greatly bene t from such a powerful technology. In fact, clinical diagnoses are frequently based on statistics and signi cant research advancements were accomplished through the recursive analysis of huge quantity of unstructured data such as in the case of X-ray images or computerized axial tomography scans. As another example, let us consider the problem of DNA sequence classi cation with the uncountable combinations that derive from such a complex structure.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/719422
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