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Ann Thorac Surg 1997;63:1529-1530
© 1997 The Society of Thoracic Surgeons

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Editorial

Thoughts and Considerations on Modeling Coronary Bypass Surgery Risk

United States Air Force Academy, USAF Academy, Colorado

The first 20% of the full text of this article appears below.

Neural network models are becoming increasingly common in medical research as evidenced by the recent surge of articles in the medical literature discussing or using neural network models [1–3]. The attraction of neural networks seems to stem from the potential for improved predictive performance, the loose biological relationship upon which neural network models were originally founded, and the impressive predictive performance reported in many articles. Lippmann and Shahian's article [4] introduces the use of neural network models in coronary artery bypass grafting (CABG) mortality prediction. They do an excellent job addressing concerns and potential limitations of neural network models as well as comparing neural network models with existing methods.

The history of neural network models began with studies that attempted to model the brain. The idea was to have many simple computational units acting in a highly interconnected manner, similar to the brain. However, neural network models presently used in prediction problems have little biological equivalence to the brain. The neural network models typically used for prediction consist of layers of simple computational units. These simple units are often called nodes or neurons (again hinting at a biological motivation). The nodes in a layer receive as input a simple weighted sum of outputs from nodes in previous layers. Each node applies a functional transformation to this weighted input . . . [Full Text of this Article]

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