About: The limited availability of resources makes the resource allocation strategy a pivotal aspect for every clinical department. Allocation is usually done on the basis of a workload estimation, which is performed by human experts. Experts have to dedicate a significant amount of time to the workload estimation, and the usefulness of estimations depends on the expert’s ability to understand very different conditions and situations. Machine learning-based predictors can help in reduce the burden on human experts, and can provide some guarantees at least in terms of repeatability of the delivered performance. However, it is unclear how good their estimations would be, compared to those of experts. In this paper we address this question by exploiting 6 algorithms for estimating the workload of future activities of a real-world department. Results suggest that this is a promising avenue for future investigations aimed to optimising the use of resources of clinical departments.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : covidontheweb.inria.fr associated with source document(s)

AttributesValues
type
value
  • The limited availability of resources makes the resource allocation strategy a pivotal aspect for every clinical department. Allocation is usually done on the basis of a workload estimation, which is performed by human experts. Experts have to dedicate a significant amount of time to the workload estimation, and the usefulness of estimations depends on the expert’s ability to understand very different conditions and situations. Machine learning-based predictors can help in reduce the burden on human experts, and can provide some guarantees at least in terms of repeatability of the delivered performance. However, it is unclear how good their estimations would be, compared to those of experts. In this paper we address this question by exploiting 6 algorithms for estimating the workload of future activities of a real-world department. Results suggest that this is a promising avenue for future investigations aimed to optimising the use of resources of clinical departments.
Subject
  • Learning
  • Resources
  • Machine learning
  • Cybernetics
  • Metrology
  • Psychometrics
  • Public finance
  • Strategic management
  • Economic planning
  • Reliability analysis
  • Schedule (project management)
  • Contexts for auctions
  • Telephone exchanges
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software