Abstract
| 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 | |
| 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 | Poland |
| City | Poznan |
| Period | 26/06/19 → 29/06/19 |
| Internet address |
Fingerprint
Cite this
}
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
ER -