Abstract
The appropriate division of data in training of computers to predict physicians\u2019 decision on blood transfusions: a reply to Dr. Sander de Bruyne
Highlights
Dr Francesco Marincola Editor-in-Chief Journal of Translational Medicine We have read the letter to the editor, written by Dr Sander de Bruyne about our paper entitled “Computer algorithm can match physicians’ decisions about blood transfusions” [1]
Dr Bruyne mentioned a well-known adequate practice to prevent the computer algorithm from overfitting and to accurately evaluate machine learning strategies, which is the separation of the sets of training and validation/test
The danger of not dividing the dataset in the training process is that the model may learn an overly specific function that performs well on the training data, but is less effective in generalizing to data outside training. In lieu of this concern, Dr Bruyne suggested that not splitting data was a problem in this study
Summary
Dr Francesco Marincola Editor-in-Chief Journal of Translational Medicine We have read the letter to the editor, written by Dr Sander de Bruyne about our paper entitled “Computer algorithm can match physicians’ decisions about blood transfusions” [1]. Dr Bruyne mentioned a well-known adequate practice to prevent the computer algorithm from overfitting and to accurately evaluate machine learning strategies, which is the separation of the sets of training and validation/test. In lieu of this concern, Dr Bruyne suggested that not splitting data was a problem in this study. *Correspondence: zryanmin@zju.edu.cn 1 Department of Anesthesiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China Full list of author information is available at the end of the article
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