Predicting 120-day hospital readmission using medical administrative patient data

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Hospitals and health-care insurers routinely use models to predict patient readmission, extrapolating from historical data. Subsequently, the predicted quantities can be used for contracting and pricing negotiations between these hospitals and healthcare insurers. The Dutch healthcare system uses unique standardized Care Trajectories (so-called DBCs) for administration and billing of care. Here, we compared supervised machine learning methods on predicting 120-day readmission as an operationally significant metric. We used administrative patient data from 21 common Care Trajectories, in combination with demographic information. A lightGBM model using undersampling to tackle class imbalance yielded an AUROC score of 0.86 and provided the highest recall score (73.8%).
Original languageEnglish
Title of host publicationProceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg
Volume2491
Publication statusPublished - 1 Jan 2019
Event28th Belgian Dutch Conference on Machine Learning - Brussels, Belgium
Duration: 6 Nov 20198 Nov 2019
Conference number: 28
https://bnaic19.brussels/benelearn-call-for-papers/

Publication series

NameCEUR Workshop Proceedings
PublisherRWTH Aachen, Informatik 5
ISSN (Print)1613-0073

Conference

Conference28th Belgian Dutch Conference on Machine Learning
Abbreviated titleBenelearn
CountryBelgium
CityBrussels
Period6/11/198/11/19
Internet address

Keywords

  • Administrative patient data
  • Care trajectory
  • Health analytics
  • Hospital readmission

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