Machine Learning

The CLAHRC WM Director has mused about machine learning before.[1] Obermeyer and Emanuel discuss this topic in the hallowed pages of the New England Journal of Medicine.[2] They point out that machine learning is already replacing radiologists, and will soon encroach on pathology. They have used machine learning in their own work in predicting death in patients with metastatic cancer. They claim that machine learning will soon be used in diagnosis, but identify two of the reasons why this will take longer than for the other uses mentioned above. First, diagnosis does not present neat outcomes (dead or alive; malignant or benign). Second, the predictive variables are unstructured in terms of availability and where they are located in a record. A third problem, not mentioned by the authors, is that data may be collected because (and only because) the clinician has suspected the diagnosis. The playing field is then tilted in favour of the machine in any comparative study. One other problem the CLAHRC WM Director has with machine learning is that the neural network in silico goes head-to-head with a human in studies. In none of the work do the authors compare the accuracy of ‘machine learning’ against standard statistical methods, such as logistic regression.

— Richard Lilford, CLAHRC WM Director


  1. Lilford RJ. Digital Future of Systematic Reviews. NIHR CLAHRC West Midlands. 16 September 2016.
  2. Obermeyer Z & Emanuel EJ. Predicting the Future – Big Data, Machine Learning, and Clinical Future. New Engl J Med. 2016; 375(13): 1216-7.

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