Use of an artificial neural network for the diagnosis of myocardial infarction.

By W. Baxt. 1991

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “In 1996, Tu [263] compared the effectiveness of artificial neural networks and logistic regression, questioning whether these techniques would replace traditional statistical methods for predicting medical outcomes such as myocardial infarction [264] or mortality [265].”
Neural networks for classification: a survey (2000, unknown): “…include bankruptcy prediction [2], [96], [101], [167], [187], [195], handwriting recognition [61], [92], [98], [100], [113], speech recognition [25], [106], product inspection [97], [130], fault detection [11], [80], medical diagnosis [19], [20], [30], [31], and bond rating [44], [163], [174].”
Artificial intelligence in retina. (2018, background): “…the same level as an expert clinician in detecting myocardial infarction in patients presenting with anterior chest pain at an emergency department (Baxt, 1991), for diagnosing renal cancer from ultrasound (Maclin et al., 1991), or for screening of diabetic retinopathy based on features extracted…”