Five Year Life Expectancy Calculator for Older Adults

Ankit Agrawal*, Jason Mathias, David Baker, Alok Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Incorporating accurate prognostic information into clinical decision making could advance evidence-based, person-centered healthcare by more effectively targeting healthcare services to those patients most likely to benefit. Here we describe the deployment of predictive models for five year life expectancy of patients, built on electronic health records (EHR) of nearly 7,500 patients aged 50 and above, with one or more visits to a large, academic, multispeciality hospital in a year, exploring more than 75 modeling configurations. The online web-Tool takes a non-redundant subset of 24 patient attributes as input and generates a patient-specific prediction of 5-year survival. The online five year life expectancy calculator is available at http://info.eecs.northwestern.edu/FiveYearLifeExpectancyCalculator.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Pages1280-1283
Number of pages4
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jul 2 2016
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume0
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1612/15/16

Keywords

  • Electronic healthcare records
  • Ensemble learning
  • Five-year life expectancy
  • Healthcare informatics
  • Supervised learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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