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Erschienen in:
Organizing for the Digital World: An Overview of Current IT Solutions to Support Individuals, Communities and Societies Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

A Giant with Feet of Clay: On the Validity of the Data that Feed Machine Learning in Medicine

verfasst von : Federico Cabitza, Davide Ciucci, Raffaele Rasoini

Erschienen in: Organizing for the Digital World

Verlag: Springer International Publishing

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Abstract

This paper considers the use of machine learning in medicine by focusing on the main problem that it has been aimed at solving or at least minimizing: uncertainty. However, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of this class of computational models, thus undermining the clinical significance of their output. Recognizing this can motivate researchers to pursue different ways to assess the value of these decision aids, as well as alternative techniques that do not “sweep uncertainty under the rug” within an objectivist fiction (which doctors can come up by trusting).

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Vorheriges Kapitel Securing National e-ID Infrastructures: Tor Networks as a Source of Threats
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Fußnoten
1
This is a vague term: here we mean data quality mainly in terms of accuracy and validity.
 
2
In what follows we introduce the concept of ML predictive model with reference to supervised discriminative (or classification) models, by far the most frequently used in medicine.
 
3
While biases are, strictly speaking, mental prejudices, idiosyncratic perceptions and cognitive behaviors producing an either impairing and distorting effect, here we rather intend the effect (by metonymy), that is the “error” in the data recorded and the decisions taken caused by the bias itself.
 
4
Moreover, Burnum traced back this lie of the land to “standards of care and a reimbursement system [that is] blind to biologic diversity”.
 
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Metadaten
Titel
A Giant with Feet of Clay: On the Validity of the Data that Feed Machine Learning in Medicine
verfasst von
Federico Cabitza
Davide Ciucci
Raffaele Rasoini
Copyright-Jahr
2019
Buch
Organizing for the Digital World

Print ISBN: 978-3-319-90502-0

Electronic ISBN: 978-3-319-90503-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-90503-7_10