Quantifying machine learning-induced overdiagnosis in sepsis
- URL: http://arxiv.org/abs/2107.10399v1
- Date: Sat, 3 Jul 2021 11:55:55 GMT
- Title: Quantifying machine learning-induced overdiagnosis in sepsis
- Authors: Anna Fedyukova, Douglas Pires, Daniel Capurro
- Abstract summary: We present an innovative approach that allows us to preemptively detect potential cases of overdiagnosis.
This is one of the first attempts to quantify machine-learning induced overdiagnosis.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The proliferation of early diagnostic technologies, including self-monitoring
systems and wearables, coupled with the application of these technologies on
large segments of healthy populations may significantly aggravate the problem
of overdiagnosis. This can lead to unwanted consequences such as overloading
health care systems and overtreatment, with potential harms to healthy
individuals. The advent of machine-learning tools to assist diagnosis -- while
promising rapid and more personalised patient management and screening -- might
contribute to this issue. The identification of overdiagnosis is usually post
hoc and demonstrated after long periods (from years to decades) and costly
randomised control trials. In this paper, we present an innovative approach
that allows us to preemptively detect potential cases of overdiagnosis during
predictive model development. This approach is based on the combination of
labels obtained from a prediction model and clustered medical trajectories,
using sepsis in adults as a test case. This is one of the first attempts to
quantify machine-learning induced overdiagnosis and we believe will serves as a
platform for further development, leading to guidelines for safe deployment of
computational diagnostic tools.
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