Humanity Is Overrated. or Not. Automatic Diagnostic Suggestions by Greg, ML (Extended Abstract)

Lapadula, Paola; Mecca, Giansalvatore; Santoro, Donatello; Solimando, Luisa; Veltri, Enzo

doi:10.1007/978-3-030-00063-9_29

Cited by 2 publications

(1 citation statement)

References 13 publications

(11 reference statements)

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“…This paper presents one of these applications, called Greg, ML. Greg, ML [15] is a research project developed by Svelto! a spin-off of the data-management group at University of Basilicata.…”

Section: Introductionmentioning

confidence: 99%

Greg, ML – Machine Learning for Healthcare at a Scale

Lapadula¹,

Mecca

Santoro

et al. 2020

Health Technol.

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This paper introduces the Greg, ML platform, a machine-learning engine and toolset conceived to generate automatic diagnostic suggestions based on patient profiles. Greg, ML departs from many other experiences in machine learning for healthcare in the fact that it was designed to handle a large number of different diagnoses, in the order of the hundreds. We discuss the architecture that stands at the core of Greg, ML, designed to handle the complex challenges posed by this ambitious goal, and confirm its effectiveness with experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned while developing the tool. In this respect, we underline that Greg, ML should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.

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Section: Introductionmentioning

confidence: 99%

Greg, ML – Machine Learning for Healthcare at a Scale

Lapadula¹,

Mecca

Santoro

et al. 2020

Health Technol.

Self Cite

View full text Add to dashboard Cite

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BUNNI: Learning Repair Actions in Rule-driven Data Cleaning

Mecca,

Papotti,

Santoro

et al. 2024

J. Data and Information Quality

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In this work, we address the challenging and open problem of involving non-expert users in the data-repairing problem as first-class citizens. Despite a large number of proposals that have been devoted to cleaning data from the point of view of expert users (IT staff and data scientists), there is a lack of studies from the perspective of non-expert ones. Given a set of available data quality rules, we exploit machine learning techniques to guide the user to identify the dirty values for each violation and repair them. We show that with a low user effort, it is possible to identify the values in tuples that can be trusted and the ones that are most likely errors. We show experimentally how this machine-learning approach leads to a unique clean solution with high quality in scenarios where other approaches fail.

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Humanity Is Overrated. or Not. Automatic Diagnostic Suggestions by Greg, ML (Extended Abstract)

Cited by 2 publications

References 13 publications

Greg, ML – Machine Learning for Healthcare at a Scale

Greg, ML – Machine Learning for Healthcare at a Scale

BUNNI: Learning Repair Actions in Rule-driven Data Cleaning

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