Editorials
What Is a Neural Network? Charles M Shufflebarger, MDo FACEP
New Horizons: Emergency Medicine at Sea Wes Young, MD, FACEP
DECEMBER 1992
21:12
ANNALS OF EMERGENCY MEDICINE
W h a t Is a N e u r a l N e t w o r k ? See related article, p 1439. "Common sense is not a simple thing. Instead, it is an immense society of h a r d - e a r n e d practical i d e a s - - o f multitudes of life-learned rules and exceptions, dispositions and tendencies, balances and checks." Marvin Minsky, The Society of Mind In this issue of Annals, Baxt presents a new method to analyze which clinical variables drive the output of an artificial neural network (neural net) trained to distinguish which patients presenting to an emergency department with anterior chest pain have an acute myocardial infarction. The article is a significant contribution not only because it clarifies the incompletely understood relationships between clinical variables and the diagnosis of acute myocardial infarction, but also because it is the first neural net contribution to the emergency medicine literature. Neural nets are an a p p r o a c h to data analysis unrelated to statistical methods. They use a mathematical p a t t e r n recognition p a r a d i g m to " l e a r n " the complex interactions between input variables and outputs as it "trains" on a known set of data. It then uses these learned patterns to estimate the expected output when presented with new inputs. The neural net thus develops "common sense," as Minsky might put it, based on a historical data base. In medicine, we use the term "clinical judgment" for such a decision based on our p r i o r learning and experience. In any event, this technology has been widely applied to problems in the engineering, computer, and finance fields. In fact, any problem in which the influencing factors have imprecise or inconsistent association with an outcome and for which a large enough historical data base exists is amenable to neural net analysis. Examples include optical character recognition software that enables digital scanners to convert scanned text documents into text files, engineering control systems, weather forecasting, and financial m a r k e t analysis. Medical applications using neural networks also have been developed. The most clinically oriented medical application to date is Baxt's acute myocardial infarction net, which estimates the likelihood of acute myocardial infarction in patients who present with chest pain.1 The net proved superior to clinical impression or statistical regression
1 46 1 / B 3
EDITORIALS
techniques for predicting acute myocardial infarction. The neural net had a diagnostic sensitivity and specificity of 97.2% and 96.2%, respectively, compared with the physician's clinical diagnostic sensitivity and specificity of 77.7% and 84.7%, respectively. The improvement was statistically significant, and better than Goldman's regression method, which achieved only 88% sensitivity and 74% specificity. 2 Neural nets are a branch of artificial intelligence developed as crude models of h u m a n neurologic function. A typical neural network is shown schematically in the Figure. In this example, numeric inputs represent d a t a provided to the network (sensory, or input nodes). In the hidden layer, each node exists as a processing element that is a function of the sum of weighted p r o d u c t of each sensory node, modulated by a threshold function. Some node outputs are positive and some negative, analogous to excitatory and inhibitory neurons. Similarly, the output node is a function of the sum of the weighted p r o d u c t of each hidden node layer, modulated by a threshold function. "Training" a neural net is the process by which the p r o p e r weights are determined for each node. The training process is complex, and few p a r a m e t e r s exist to guide the neural net p r o g r a m m e r in choosing an optimal training algorithm. Yet the subtleties of training are irrelevant to a validated neural net. Neural nets are also analogous to human judgment in that the driving forces for malting a decision are often difficult to quantify. In human decision making, this is due to the inability of most experts to be able to quantify the influences of each complex factor that drives a decision. 3 Such influences are melded into an overall pattern that is identifiable, although the processing of the individual influences is in the subconscious. Similarly, the influence of each input in a neural net is difficult to quantitate. This is due to the massively parallel structure of a multiple hidden layer, multiple hidden node network. Stated differently using an example from Baxt's acute myocardial infarction network, the question might be "What is the influence of the presence of syncope on the likelihood of acute myocardial infarction?" The answer is that it is dependent on whether rales, ST elevation, jugular venous Figure. Schematic of a 3 x 2 x I neural network, li, inputs; Hi, hidden nodes; and Oi, network output. Mathematically, Hi=f(i~=iIi" W h i ) where H i is the value o f the ith hidden node, I i is the value o f the ith input node, and Whl is the weighing factor between I s and H i.
Inputs ~dde~Layer
distension, and so forth, were present. Baxt's present analysis is important because it presents a new method for determining the impact of each input. As a contribution to the literature concerned with the problem of diagnosing acute myocardial infarction, the Baxt net is significant not only because it is more accurate than existing regression techniques or a clinician's judgment, but also as a foundation for use with other new methods such as biochemical m a r k e r s or imaging studies, which can easily be added into a neural network for diagnosing acute myocardial infarction. The addition of these variables can only improve the accuracy of the network. The new method for analyzing the driving forces of the net then can be used to determine which of the variables are important and which are not. Such associations are frequently i n a p p a r e n t using statistical methods. Baxt found, for example, that the presence of rales, syncope, jugular venous distension, response to trinitroglycerin, and nausea and vomiting all contributed heavily to the network output, each more than dyspnea or diaphoresis. The presence of tales contributed even more strongly to the net prediction than did ST changes. He stresses that these results must be i n t e r p r e t e d with caution, because of context sensitivity. That is, many patterns of symptoms and signs of acute myocardial infarction may not have been represented among the 706 patients analyzed by the network. A larger cohort may provide a more reliable indication of the importance of each variable. Baxt does not address the possible effect of over-training, a complication of neural network analysis that can easily occur in complex networks, is difficult to quantitate, and would affect, at least to some degree, the weights determined by the network. Still, this type of analysis can allow us to refocus our attention on the most critical information when assessing the patient with chest pain. By identifying patterns of clinical data in which the results of other tests do not change the prediction, it may even identify patients who should not have further diagnostic procedures. Neural networks are a foreign subject to most of us, who are intimidated by statistical analysis and computers. Indeed, there is nothing enigmatic about neural net techniques, which are neither new nor poorly described. They cannot replace expert clinical judgment but may provide an inexpensive aid for decision making. In addition, there is an a r r a y of clinical problems whose solutions may be unraveled by the judicious use of neural network analysis and by the application of the new methodology presented by Baxt. P e r h a p s the apphcatiou of his technique will have a clinical and cost-saving impact and aid us in understanding the vagaries of medical decision making and common sense. Charles M Shufflebarger, MD, FACEP Emergency Medicine and Trauma Center Methodist Hospital of Indiana Indianapolis 1. Baxt WG: Use of an artificial neural network for the diagnosis of myocardial infarotie n. Ann InternMed 1991;115:843-84& 2. Goldman L, Cook EF, Brand DA, et al: A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N EnglJ Mefl 1988;318:797-803. 3. Kurzweil R: TheAge of IntelligentMachines. Cambridge, Massachusetts, The MIT Press, 1990, p 299-303.
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NOVEMBER1992
What Is a Neural Network? Charles M Shufflebarger, MDo FACEP
New Horizons: Emergency Medicine at Sea Wes Young, MD, FACEP
DECEMBER 1992
21:12
ANNALS OF EMERGENCY MEDICINE
W h a t Is a N e u r a l N e t w o r k ? See related article, p 1439. "Common sense is not a simple thing. Instead, it is an immense society of h a r d - e a r n e d practical i d e a s - - o f multitudes of life-learned rules and exceptions, dispositions and tendencies, balances and checks." Marvin Minsky, The Society of Mind In this issue of Annals, Baxt presents a new method to analyze which clinical variables drive the output of an artificial neural network (neural net) trained to distinguish which patients presenting to an emergency department with anterior chest pain have an acute myocardial infarction. The article is a significant contribution not only because it clarifies the incompletely understood relationships between clinical variables and the diagnosis of acute myocardial infarction, but also because it is the first neural net contribution to the emergency medicine literature. Neural nets are an a p p r o a c h to data analysis unrelated to statistical methods. They use a mathematical p a t t e r n recognition p a r a d i g m to " l e a r n " the complex interactions between input variables and outputs as it "trains" on a known set of data. It then uses these learned patterns to estimate the expected output when presented with new inputs. The neural net thus develops "common sense," as Minsky might put it, based on a historical data base. In medicine, we use the term "clinical judgment" for such a decision based on our p r i o r learning and experience. In any event, this technology has been widely applied to problems in the engineering, computer, and finance fields. In fact, any problem in which the influencing factors have imprecise or inconsistent association with an outcome and for which a large enough historical data base exists is amenable to neural net analysis. Examples include optical character recognition software that enables digital scanners to convert scanned text documents into text files, engineering control systems, weather forecasting, and financial m a r k e t analysis. Medical applications using neural networks also have been developed. The most clinically oriented medical application to date is Baxt's acute myocardial infarction net, which estimates the likelihood of acute myocardial infarction in patients who present with chest pain.1 The net proved superior to clinical impression or statistical regression
1 46 1 / B 3
EDITORIALS
techniques for predicting acute myocardial infarction. The neural net had a diagnostic sensitivity and specificity of 97.2% and 96.2%, respectively, compared with the physician's clinical diagnostic sensitivity and specificity of 77.7% and 84.7%, respectively. The improvement was statistically significant, and better than Goldman's regression method, which achieved only 88% sensitivity and 74% specificity. 2 Neural nets are a branch of artificial intelligence developed as crude models of h u m a n neurologic function. A typical neural network is shown schematically in the Figure. In this example, numeric inputs represent d a t a provided to the network (sensory, or input nodes). In the hidden layer, each node exists as a processing element that is a function of the sum of weighted p r o d u c t of each sensory node, modulated by a threshold function. Some node outputs are positive and some negative, analogous to excitatory and inhibitory neurons. Similarly, the output node is a function of the sum of the weighted p r o d u c t of each hidden node layer, modulated by a threshold function. "Training" a neural net is the process by which the p r o p e r weights are determined for each node. The training process is complex, and few p a r a m e t e r s exist to guide the neural net p r o g r a m m e r in choosing an optimal training algorithm. Yet the subtleties of training are irrelevant to a validated neural net. Neural nets are also analogous to human judgment in that the driving forces for malting a decision are often difficult to quantify. In human decision making, this is due to the inability of most experts to be able to quantify the influences of each complex factor that drives a decision. 3 Such influences are melded into an overall pattern that is identifiable, although the processing of the individual influences is in the subconscious. Similarly, the influence of each input in a neural net is difficult to quantitate. This is due to the massively parallel structure of a multiple hidden layer, multiple hidden node network. Stated differently using an example from Baxt's acute myocardial infarction network, the question might be "What is the influence of the presence of syncope on the likelihood of acute myocardial infarction?" The answer is that it is dependent on whether rales, ST elevation, jugular venous Figure. Schematic of a 3 x 2 x I neural network, li, inputs; Hi, hidden nodes; and Oi, network output. Mathematically, Hi=f(i~=iIi" W h i ) where H i is the value o f the ith hidden node, I i is the value o f the ith input node, and Whl is the weighing factor between I s and H i.
Inputs ~dde~Layer
distension, and so forth, were present. Baxt's present analysis is important because it presents a new method for determining the impact of each input. As a contribution to the literature concerned with the problem of diagnosing acute myocardial infarction, the Baxt net is significant not only because it is more accurate than existing regression techniques or a clinician's judgment, but also as a foundation for use with other new methods such as biochemical m a r k e r s or imaging studies, which can easily be added into a neural network for diagnosing acute myocardial infarction. The addition of these variables can only improve the accuracy of the network. The new method for analyzing the driving forces of the net then can be used to determine which of the variables are important and which are not. Such associations are frequently i n a p p a r e n t using statistical methods. Baxt found, for example, that the presence of rales, syncope, jugular venous distension, response to trinitroglycerin, and nausea and vomiting all contributed heavily to the network output, each more than dyspnea or diaphoresis. The presence of tales contributed even more strongly to the net prediction than did ST changes. He stresses that these results must be i n t e r p r e t e d with caution, because of context sensitivity. That is, many patterns of symptoms and signs of acute myocardial infarction may not have been represented among the 706 patients analyzed by the network. A larger cohort may provide a more reliable indication of the importance of each variable. Baxt does not address the possible effect of over-training, a complication of neural network analysis that can easily occur in complex networks, is difficult to quantitate, and would affect, at least to some degree, the weights determined by the network. Still, this type of analysis can allow us to refocus our attention on the most critical information when assessing the patient with chest pain. By identifying patterns of clinical data in which the results of other tests do not change the prediction, it may even identify patients who should not have further diagnostic procedures. Neural networks are a foreign subject to most of us, who are intimidated by statistical analysis and computers. Indeed, there is nothing enigmatic about neural net techniques, which are neither new nor poorly described. They cannot replace expert clinical judgment but may provide an inexpensive aid for decision making. In addition, there is an a r r a y of clinical problems whose solutions may be unraveled by the judicious use of neural network analysis and by the application of the new methodology presented by Baxt. P e r h a p s the apphcatiou of his technique will have a clinical and cost-saving impact and aid us in understanding the vagaries of medical decision making and common sense. Charles M Shufflebarger, MD, FACEP Emergency Medicine and Trauma Center Methodist Hospital of Indiana Indianapolis 1. Baxt WG: Use of an artificial neural network for the diagnosis of myocardial infarotie n. Ann InternMed 1991;115:843-84& 2. Goldman L, Cook EF, Brand DA, et al: A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N EnglJ Mefl 1988;318:797-803. 3. Kurzweil R: TheAge of IntelligentMachines. Cambridge, Massachusetts, The MIT Press, 1990, p 299-303.
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NOVEMBER1992
