Artificial Intelligence

Publication Title: 
Artificial Intelligence in Medicine

OBJECTIVE: Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS).

Author(s): 
Zhou, Xuezhong
Chen, Shibo
Liu, Baoyan
Zhang, Runsun
Wang, Yinghui
Li, Ping
Guo, Yufeng
Zhang, Hua
Gao, Zhuye
Yan, Xiufeng
Publication Title: 
Biological Cybernetics

Optimization of performance in collective systems often requires altruism. The emergence and stabilization of altruistic behaviors are difficult to achieve because the agents incur a cost when behaving altruistically. In this paper, we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism.

Author(s): 
Zamora, J.
Mill·n, J. R.
Murciano, A.
Publication Title: 
Artificial Life

Evolutionary theorists have long been interested in the conditions that permit the evolution of altruistic cooperation. Recent work has demonstrated that altruistic donation can evolve in surprisingly simple models, in which agents base their decisions to donate solely on the similarity of evolved "tags" relative to evolved tag-difference tolerances. There is disagreement, however, about the conditions under which tag-mediated altruism will in fact evolve.

Author(s): 
Spector, Lee
Klein, Jon
Publication Title: 
Artificial Life

As part of our research on programmed self-decomposition, we formed the hypothesis that originally immortal terrestrial organisms evolve into ones that are programmed for autonomous death. We then conducted evolutionary simulation experiments in which we examined this hypothesis using an artificial ecosystem that we designed to resemble a terrestrial ecosystem endowed with artificial chemistry.

Author(s): 
Oohashi, Tsutomu
Maekawa, Tadao
Ueno, Osamu
Kawai, Norie
Nishina, Emi
Honda, Manabu
Publication Title: 
IEEE transactions on cybernetics

Discriminating altruism, particularly kin altruism, is a fundamental mechanism of cooperation in nature. Altruistic behavior is not favored by evolution in the circumstances where there are "kin cheaters" that cannot be effectively identified. Using evolutionary iterated prisoner's dilemma, we deduce the condition for discriminating strategies to be evolutionarily stable and show that the competition between groups of different discriminating strategies restrains the percentage of kin cheaters.

Author(s): 
Li, Jiawei
Kendall, Graham
Publication Title: 
Scientific Reports

Cooperation in one-shot anonymous interactions is a widely documented aspect of human behaviour. Here we shed light on the motivations behind this behaviour by experimentally exploring cooperation in a one-shot continuous-strategy Prisoner's Dilemma (i.e. one-shot two-player Public Goods Game). We examine the distribution of cooperation amounts, and how that distribution varies based on the benefit-to-cost ratio of cooperation (b/c). Interestingly, we find a trimodal distribution at all b/c values investigated.

Author(s): 
Capraro, Valerio
Jordan, Jillian J.
Rand, David G.
Publication Title: 
Anesthesia and Analgesia

BACKGROUND: Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable. Model-based and proportional-integral-derivative (PID) controllers outperform manual control. We investigated the application of reinforcement learning (RL), an intelligent systems control method, to closed-loop BIS-guided, propofol-induced hypnosis in simulated intraoperative patients. We also compared the performance of the RL agent against that of a conventional PID controller.

Author(s): 
Moore, Brett L.
Quasny, Todd M.
Doufas, Anthony G.
Publication Title: 
Anesthesia and Analgesia

Reinforcement learning (RL) is an intelligent systems technique with a history of success in difficult robotic control problems. Similar machine learning techniques, such as artificial neural networks and fuzzy logic, have been successfully applied to clinical control problems. Although RL presents a mathematically robust method of achieving optimal control in systems challenged with noise, nonlinearity, time delay, and uncertainty, no application of RL in clinical anesthesia has been reported.

Author(s): 
Moore, Brett L.
Doufas, Anthony G.
Pyeatt, Larry D.
Publication Title: 
The International Journal of Clinical and Experimental Hypnosis

Contemporary studies in the cognitive neuroscience of attention and suggestion shed new light on the underlying neural mechanisms that operationalize these effects. Without adhering to important caveats inherent to imaging of the living human brain, however, findings from brain imaging studies may enthrall more than explain. Scholars, practitioners, professionals, and consumers must realize that the influence words exert on focal brain activity is measurable but that these measurements are often difficult to interpret.

Author(s): 
Raz, Amir
Publication Title: 
Biomedizinische Technik. Biomedical Engineering

INTRODUCTION: Measuring and ensuring an adequate level of analgesia in patients are of increasing interest in the area of automated drug delivery during general anesthesia. Therefore, the aim of this investigation was to develop a control system that may reflect the intraoperative analgesia value. Our hypothesis was that a feedback controller could be applied in clinical practice safely and at an adequate quality of analgesia. The purpose of this study was to evaluate the practical feasibility of such a system in a clinical setting.

Author(s): 
Janda, Matthias
Schubert, Agnes
Bajorat, Jörn
Hofmockel, Rainer
Nöldge-Schomburg, Gabriele F. E.
Lampe, Bernhard P.
Simanski, Olaf

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