Patient stratification using longitudinal data – application of latent class mixed models

Nophar Geifman, Hannah Lennon, Niels Peek

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Analysis of longitudinal data in medical research is becoming increasingly important, in particular for the identification of patient subgroups, as the focus of medical research is shifting toward personalised medicine. Here we present the use of a statistical learning approach for the identification of subgroups of hypertension patients demonstrating different patterns of response to treatment. This method, applied to large-scale patient-level data, has identified three such groups found to be associated with different clinical characteristics. We further consider the utility of this method in medical research by comparison to the application in two additional studies.

Original languageEnglish
Title of host publicationBuilding Continents of Knowledge in Oceans of Data
Subtitle of host publicationThe Future of Co-Created eHealth - Proceedings of MIE 2018
PublisherIOS Press
Pages176-180
Number of pages5
Volume247
ISBN (Electronic)9781614998518
ISBN (Print)9781614998518
DOIs
Publication statusPublished - 2018
Event40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, Sweden
Duration: 24 Apr 201826 Apr 2018

Publication series

NameStudies in Health Technology and Informatics
Volume247

Conference

Conference40th Medical Informatics in Europe Conference, MIE 2018
Country/TerritorySweden
CityGothenburg
Period24/04/1826/04/18

Keywords

  • Personalised medicine
  • Statistical learning
  • Subgroup discovery

Fingerprint

Dive into the research topics of 'Patient stratification using longitudinal data – application of latent class mixed models'. Together they form a unique fingerprint.

Cite this