Inferring temporal phenotypes with topological data analysis and pseudo time-series

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

  • External authors:
  • John H. Holmes
  • Lucia Sacchi
  • Seyed Erfan Sajjadi
  • Allan Tucker


Temporal phenotyping enables clinicians to better under-stand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable clinicians to identify early warning signs of progression in specific sub-types and therefore to make informed decisions tailored to individual patients. In this paper, we explore two approaches to building temporal phenotypes based on the topology of data: topological data analysis and pseudo time-series. Using type 2 diabetes data, we show that the topological data analysis approach is able to identify trajectories representing different temporal phenotypes and that pseudo time-series can infer a state space model characterized by transitions between hidden states that represent distinct temporal phenotypes. Both approaches highlight lipid profiles as key factors in distinguishing the phenotypes.

Bibliographical metadata

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings
EditorsSzymon Wilk, Annette ten Teije, David Riaño
PublisherSpringer Nature
Number of pages11
ISBN (Print)9783030216412
Publication statusPublished - 2019
Event17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland
Event duration: 26 Jun 201929 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th Conference on Artificial Intelligence in Medicine, AIME 2019