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Modeling Medical Treatment Using Markov Decision Processes
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In the citing paper
“Currently, most treatment decisions in the medical domain are made via ad-hoc or heuristic approaches, but there is a growing body of evidence that such complex treatment decisions are better handled through modeling rather than intuition alone [18,21].”
From Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach (2013) · cites this paper as [18]
In the citing paper
“Markov decision processes (MDPs) are one efficient technique for determining such optimal sequential decisions (termed a 'policy') in dynamic and uncertain environments [18,19], and have been explored in medical decision-making problems in recent years [18,20].”
From Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach (2013) · cites this paper as [18]
In the citing paper
“As noted elsewhere, the collection of such 'natural history' disease data is fraught with many challenges, ethical and otherwise, particularly if such untreated conditions pose significant health risks [18].”
From Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach (2013) · cites this paper as [18]
In the citing paper
“However, despite such applicability of sequential decisionmaking techniques like MDPs to medical decision-making, there have been relatively few applications in healthcare [18,19].”
From Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach (2013) · cites this paper as [18]
In the citing paper
“The work presented here adds to a growing body of evidence that such complex treatment decisions may be better handled through modeling than intuition alone [18,21].”
From Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach (2013) · cites this paper as [18]
In the citing paper
“MDPs (and their partially observable cousins) directly address many of the challenges faced in clinical decision-making [17,18].”
From Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach (2013) · cites this paper as [18]
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Metadata
- Authors
- Schaefer, A. J., Bailey, M. D., Shechter, S. M., & Roberts, M. S.
- Affiliations
- University of Pittsburgh (US)
- Venue
- International Series in Operations Research & Management Science
- Publisher
- Springer US
- Published
- 2005
- Pages
- 593-612
- ISSN
- 0884-8289 (ISSN-L)
- OpenAlex
- W58990062
- Cited by
- 155
- References
- 53
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 11 · 2023: 13 · 2022: 6 · 2021: 13 · 2020: 8 · 2019: 17 · 2018: 15 · 2017: 9 · 2016: 5 · 2015: 12 · 2014: 3 · 2013: 6 · 2012: 5
- FWCI
- 8.16
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- International series in management science/operations research/International series in operations research & management science (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Decision Sciences › Management Science and Operations Research › Complex Systems and Decision Making
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Dynamic Programming Control
- Keywords
- Markov decision process, treatment decision-making, sequential decision making, stochastic dynamic programming, treatment planning, clinical decision support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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