Affordable Access

Access to the full text

Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings

Authors
  • Seveso, Andrea1
  • Campagner, Andrea2
  • Ciucci, Davide1
  • Cabitza, Federico1
  • 1 Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy , Milan (Italy)
  • 2 IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, Milan, 20161, Italy , Milan (Italy)
Type
Published Article
Journal
BMC Medical Informatics and Decision Making
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Aug 20, 2020
Volume
20
Issue
Suppl 5
Identifiers
DOI: 10.1186/s12911-020-01152-8
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundDespite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. In this paper, we focus on the representation of a general and wide-spread medical terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (for instance, pain, visible signs), ranging from Absent to Extreme. Specifically, we will study how both potential patients and doctors perceive the different levels of the terminology in both quantitative and qualitative terms, and if the embedded user knowledge could improve the representation of ordinal values in the construction of machine learning models.MethodsTo this aim, we conducted a questionnaire-based research study involving a relatively large sample of 1,152 potential patients and 31 clinicians to represent numerically the perceived meaning of standard and widely-applied labels to describe health conditions. Using these collected values, we then present and discuss different possible fuzzy-set based representations that address the vagueness of medical interpretation by taking into account the perceptions of domain experts. We also apply the findings of this user study to evaluate the impact of different encodings on the predictive performance of common machine learning models in regard to a real-world medical prognostic task.ResultsWe found significant differences in the perception of pain levels between the two user groups. We also show that the proposed encodings can improve the performances of specific classes of models, and discuss when this is the case.ConclusionsIn perspective, our hope is that the proposed techniques for ordinal scale representation and ordinal encoding may be useful to the research community, and also that our methodology will be applied to other widely used ordinal scales for improving validity of datasets and bettering the results of machine learning tasks.

Report this publication

Statistics

Seen <100 times