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Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift

An individual patient is not the average representative of the population. Rather he or she is a person with unique characteristics. An intervention may be effective for a population but not necessarily for the individual patient. The recommendation of a guideline may not be right for a particular p...

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Detalles Bibliográficos
Autor principal: Grossi, Enzo
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3024877/
https://www.ncbi.nlm.nih.gov/pubmed/21172062
http://dx.doi.org/10.1186/1742-4933-7-S1-S3
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author Grossi, Enzo
author_facet Grossi, Enzo
author_sort Grossi, Enzo
collection PubMed
description An individual patient is not the average representative of the population. Rather he or she is a person with unique characteristics. An intervention may be effective for a population but not necessarily for the individual patient. The recommendation of a guideline may not be right for a particular patient because it is not what he or she wants, and implementing the recommendation will not necessarily mean a favourable outcome. The author will describe a reconfiguration of medical thought which originates from non linear dynamics and chaos theory. The coupling of computer science and these new theoretical bases coming from complex systems mathematics allows the creation of “intelligent” agents able to adapt themselves dynamically to problem of high complexity: the Artificial Adaptive Systems, which include Artificial Neural Networks( ANNs ) and Evolutionary Algorithms ( EA). ANNs and EA are able to reproduce the dynamical interaction of multiple factors simultaneously, allowing the study of complexity; they can also help medical doctors in making decisions under extreme uncertainty and to draw conclusions on individual basis and not as average trends. These tools can allow a more efficient Technology Transfer from the Science of Medicine to the Real World overcoming many obstacles responsible for the present translational failure. They also contribute to a new holistic vision of the human subject contrasting the statistical reductionism which tends to squeeze or even delete the single subject sacrificing him to his group of belongingness. A remarkable contribution to this individual approach comes from Fuzzy Logic, according to which there are no sharp limits between opposite things, like health and disease. This approach allows to partially escape from probability theory trap in situations where is fundamental to express a judgment based on a single case and favours a novel humanism directed to the management of the patient as individual subject.
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spelling pubmed-30248772011-01-22 Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift Grossi, Enzo Immun Ageing Proceedings An individual patient is not the average representative of the population. Rather he or she is a person with unique characteristics. An intervention may be effective for a population but not necessarily for the individual patient. The recommendation of a guideline may not be right for a particular patient because it is not what he or she wants, and implementing the recommendation will not necessarily mean a favourable outcome. The author will describe a reconfiguration of medical thought which originates from non linear dynamics and chaos theory. The coupling of computer science and these new theoretical bases coming from complex systems mathematics allows the creation of “intelligent” agents able to adapt themselves dynamically to problem of high complexity: the Artificial Adaptive Systems, which include Artificial Neural Networks( ANNs ) and Evolutionary Algorithms ( EA). ANNs and EA are able to reproduce the dynamical interaction of multiple factors simultaneously, allowing the study of complexity; they can also help medical doctors in making decisions under extreme uncertainty and to draw conclusions on individual basis and not as average trends. These tools can allow a more efficient Technology Transfer from the Science of Medicine to the Real World overcoming many obstacles responsible for the present translational failure. They also contribute to a new holistic vision of the human subject contrasting the statistical reductionism which tends to squeeze or even delete the single subject sacrificing him to his group of belongingness. A remarkable contribution to this individual approach comes from Fuzzy Logic, according to which there are no sharp limits between opposite things, like health and disease. This approach allows to partially escape from probability theory trap in situations where is fundamental to express a judgment based on a single case and favours a novel humanism directed to the management of the patient as individual subject. BioMed Central 2010-12-16 /pmc/articles/PMC3024877/ /pubmed/21172062 http://dx.doi.org/10.1186/1742-4933-7-S1-S3 Text en Copyright ©2010 Grossi; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Grossi, Enzo
Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift
title Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift
title_full Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift
title_fullStr Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift
title_full_unstemmed Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift
title_short Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift
title_sort artificial adaptive systems and predictive medicine: a revolutionary paradigm shift
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3024877/
https://www.ncbi.nlm.nih.gov/pubmed/21172062
http://dx.doi.org/10.1186/1742-4933-7-S1-S3
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