The importance of artificial neural networks in biomedicine
- New York Medical College, Valhalla, NY (United States)
The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is difficult for traditional statistical methods to capture complex systems because traditional methods attempt to find the model that best fits the statistician`s understanding of the phenomenon; complex systems are difficult to understand and therefore difficult to fit with a simple model. Artificial neural networks are nonparametric regression models. They can capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. Further, artificial neural networks can be represented, not only as formulae, but also as graphical models. Graphical models can increase analytic power and flexibility. Artificial neural networks are a powerful method for capturing complex phenomena, but their use requires a paradigm shift, from exploratory analysis of the data to exploratory analysis of the model.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- OSTI ID:
- 377060
- Report Number(s):
- PNL-SA-26375; CONF-9503142-; ON: DE96009360; TRN: 96:003982-0017
- Resource Relation:
- Conference: Workshop on environmental and energy applications of neural networks conference, Richland, WA (United States), 30-31 Mar 1995; Other Information: PBD: [1995]; Related Information: Is Part Of Applications of neural networks in environmental and energy sciences and engineering. Proceedings of the 1995 workshop on environmental and energy applications of neural networks; Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.; PB: 193 p.
- Country of Publication:
- United States
- Language:
- English
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