A Data-Driven Exploration of Hypotheses on Disease Dynamics

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

doi:10.1007/978-3-030-21642-9_23

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 Line Resilience (part of LIRS program)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-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 language	English
Title of host publication	Artificial Intelligence in Medicine
Subtitle of host publication	17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings
Editors	David Riaño, Szymon Wilk, Annette ten Teije
Place of Publication	Cham
Publisher	Springer International Publishing AG
Chapter	23
Pages	170-179
Number of pages	10
ISBN (Electronic)	9783030216429
ISBN (Print)	9783030216412
DOIs	https://doi.org/10.1007/978-3-030-21642-9_23
Publication status	Published - 30 May 2019
Event	17th Conference on Artificial Intelligence in Medicine - Poznan, Poland Duration: 26 Jun 2019 → 29 Jun 2019 https://aime19.aimedicine.info/

Publication series

Series	Lecture Notes in Computer Science (LNCS)
Volume	11526
ISSN	0302-9743

Conference

Conference	17th Conference on Artificial Intelligence in Medicine
Abbreviated title	AIME 2019
Country/Territory	Poland
City	Poznan
Period	26/06/19 → 29/06/19
Internet address	https://aime19.aimedicine.info/

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10.1007/978-3-030-21642-9_23

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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). 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.",

author = "Bueno, {Marcos L. P.} and Arjen Hommersom and Lucas, {Peter J. F.} and Joost Janzing",

year = "2019",

month = may,

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language = "English",

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series = "Lecture Notes in Computer Science (LNCS)",

publisher = "Springer International Publishing AG",

pages = "170--179",

editor = "David Ria{\~n}o and Szymon Wilk and {ten Teije}, Annette",

booktitle = "Artificial Intelligence in Medicine",

address = "Switzerland",

note = "17th Conference on Artificial Intelligence in Medicine, AIME 2019 ; Conference date: 26-06-2019 Through 29-06-2019",

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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 proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - A Data-Driven Exploration of Hypotheses on Disease Dynamics

AU - Bueno, Marcos L. P.

AU - Hommersom, Arjen

AU - Lucas, Peter J. F.

AU - Janzing, Joost

PY - 2019/5/30

Y1 - 2019/5/30

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.

U2 - 10.1007/978-3-030-21642-9_23

DO - 10.1007/978-3-030-21642-9_23

M3 - Conference article in proceeding

SN - 9783030216412

T3 - Lecture Notes in Computer Science (LNCS)

SP - 170

EP - 179

BT - Artificial Intelligence in Medicine

A2 - Riaño, David

A2 - Wilk, Szymon

A2 - ten Teije, Annette

PB - Springer International Publishing AG

CY - Cham

T2 - 17th Conference on Artificial Intelligence in Medicine

Y2 - 26 June 2019 through 29 June 2019

ER -

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

A Data-Driven Exploration of Hypotheses on Disease Dynamics

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