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 article in proceedingAcademicpeer-review

Abstract

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
EditorsDavid Riaño, Szymon Wilk, Annette ten Teije
Place of PublicationCham
PublisherSpringer International Publishing AG
Chapter23
Pages170-179
Number of pages10
ISBN (Electronic)9783030216429
ISBN (Print)9783030216412
DOIs
Publication statusPublished - 30 May 2019
Event17th Conference on Artificial Intelligence in Medicine - Poznan, Poland
Duration: 26 Jun 201929 Jun 2019
https://aime19.aimedicine.info/

Publication series

SeriesLecture Notes in Computer Science (LNCS)
Volume11526
ISSN0302-9743

Conference

Conference17th Conference on Artificial Intelligence in Medicine
Abbreviated titleAIME 2019
CountryPoland
CityPoznan
Period26/06/1929/06/19
Internet address

Fingerprint

Unsupervised learning
Hidden Markov models

Cite this

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., & Janzing, J. (2019). A Data-Driven Exploration of Hypotheses on Disease Dynamics. In D. Riaño, S. Wilk, & A. ten Teije (Eds.), Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings (pp. 170-179). Cham: Springer International Publishing AG. Lecture Notes in Computer Science (LNCS), Vol.. 11526 https://doi.org/10.1007/978-3-030-21642-9_23
Bueno, Marcos L. P. ; Hommersom, Arjen ; Lucas, Peter J. F. ; Janzing, Joost. / A Data-Driven Exploration of Hypotheses on Disease Dynamics. Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. editor / David Riaño ; Szymon Wilk ; Annette ten Teije. Cham : Springer International Publishing AG, 2019. pp. 170-179 (Lecture Notes in Computer Science (LNCS), Vol. 11526).
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title = "A Data-Driven Exploration of Hypotheses on Disease Dynamics",
abstract = "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.",
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Bueno, MLP, Hommersom, A, Lucas, PJF & Janzing, J 2019, A Data-Driven Exploration of Hypotheses on Disease Dynamics. in D Riaño, S Wilk & A ten Teije (eds), Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. Springer International Publishing AG, Cham, Lecture Notes in Computer Science (LNCS), vol. 11526, pp. 170-179, 17th Conference on Artificial Intelligence in Medicine, Poznan, Poland, 26/06/19. https://doi.org/10.1007/978-3-030-21642-9_23

A Data-Driven Exploration of Hypotheses on Disease Dynamics. / Bueno, Marcos L. P.; Hommersom, Arjen; Lucas, Peter J. F.; Janzing, Joost.

Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. ed. / David Riaño; Szymon Wilk; Annette ten Teije. Cham : Springer International Publishing AG, 2019. p. 170-179 (Lecture Notes in Computer Science (LNCS), Vol. 11526).

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

TY - GEN

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N2 - 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.

AB - 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.

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T3 - Lecture Notes in Computer Science (LNCS)

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BT - Artificial Intelligence in Medicine

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Bueno MLP, Hommersom A, Lucas PJF, Janzing J. A Data-Driven Exploration of Hypotheses on Disease Dynamics. In Riaño D, Wilk S, ten Teije A, editors, Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. Cham: Springer International Publishing AG. 2019. p. 170-179. (Lecture Notes in Computer Science (LNCS), Vol. 11526). https://doi.org/10.1007/978-3-030-21642-9_23