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Informing sequential clinical decision-making through reinforcement learning: an empirical study
| Content Provider | PubMed Central |
|---|---|
| Author | Shortreed, Susan M. Laber, Eric Lizotte, Daniel J. Stroup, T. Scott Pineau, Joelle Murphy, Susan A. |
| Abstract | This paper highlights the role that reinforcement learning can play in the optimization of treatment policies for chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting, we must first tackle a number of challenges. We outline some of these challenges and present methods for overcoming them. First, we describe a multiple imputation approach to overcome the problem of missing data. Second, we discuss the use of function approximation in the context of a highly variable observation set. Finally, we discuss approaches to summarizing the evidence in the data for recommending a particular action and quantifying the uncertainty around the Q-function of the recommended policy. We present the results of applying these methods to real clinical trial data of patients with schizophrenia. |
| Related Links | http://dx.doi.org/10.1007/s10994-010-5229-0 |
| Starting Page | 109 |
| File Format | |
| ISSN | 15730565 |
| e-ISSN | 15730565 |
| Journal | Machine learning |
| Issue Number | 1-2 |
| Volume Number | 84 |
| Language | English |
| Publisher Date | 2011-07-01 |
| Access Restriction | Open |
| Subject Keyword | Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Software |
