A Data-Driven Exploration of Hypotheses on Disease Dynamics

Marcos L.P. Bueno*, Arjen Hommersom, Peter J.F. Lucas, Joost Janzing

*Corresponding author for this work

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


Unsupervised learning is often used to obtain insight into the underlying structure of medical data. In this paper, we show that unsupervised methods, in particular hidden Markov models, can go beyond this by guiding the generation of clinical outcome measures and hypotheses, which play a crucial role in medical research. The usage of the data-driven approach facilitates selecting which hypotheses to further investigate. We demonstrate this by using clinical trial data for psychotic depression treatment as a case study. The discovered latent structure and proposed outcome are shown to provide new insight into the heterogeneity of psychotic depression in terms of predictive symptoms.
Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine
Subtitle of host publication17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings
Place of PublicationCham
ISBN (Electronic)978-3-030-21642-9
ISBN (Print)978-3-030-21641-2
Publication statusPublished - 2019
Externally publishedYes
Event17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland
Duration: 26 Jun 201929 Jun 2019
Conference number: 17

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th Conference on Artificial Intelligence in Medicine, AIME 2019
Abbreviated titleAIME


  • Artificial intelligence
  • Bayesian networks
  • Machine learning
  • Medical informatics
  • n/a OA procedure


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