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 |
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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 |
Place of Publication | Cham |
Publisher | Springer |
Pages | 170-179 |
ISBN (Electronic) | 978-3-030-21642-9 |
ISBN (Print) | 978-3-030-21641-2 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland Duration: 26 Jun 2019 → 29 Jun 2019 Conference number: 17 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 11526 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 17th Conference on Artificial Intelligence in Medicine, AIME 2019 |
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Abbreviated title | AIME |
Country/Territory | Poland |
City | Poznan |
Period | 26/06/19 → 29/06/19 |
Keywords
- Artificial intelligence
- Bayesian networks
- Machine learning
- Medical informatics
- n/a OA procedure