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Artificial intelligence and outcome research
Author(s) -
Grossi Enzo,
Buscema Massimo
Publication year - 2006
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.20081
Subject(s) - outcome (game theory) , artificial intelligence , computer science , judgement , observational study , fuzzy logic , management science , cognitive science , machine learning , risk analysis (engineering) , psychology , epistemology , mathematics , medicine , engineering , mathematical economics , philosophy , statistics
The coupling of computer science and theoretical bases like non‐linear dynamics and chaos, quite new for medicine theory, allows the creation of “intelligent” agents (Artificial Adaptive Systems [AAS]) able to adapt themselves dynamically to problems of high complexity. ASS are able to reproduce the dynamical interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on an individual basis and not as average trends. These tools can offer specific advantages within the outcome research arena, helping to answer some open issues like enhancing the internal validity of observational studies, transferring evidence derived from clinical research to a single patient level, and performing “virtual” clinical trials as a guide for more efficient clinical development. A remarkable contribution to this individual approach comes from Fuzzy Logic, according to which there are no sharp limits between opposite things, like wealth and disease. This approach allows for partially escaping from the probability theory trap in situations where it is fundamental to express a judgement based on a single case and favour a novel humanism directed to the management of the patient as an individual subject. Some examples of original applications in the authors' experience are described. Drug Dev. Res. 67:227–244, 2006. © 2006 Wiley‐Liss, Inc.