CoChat
Sign Up
Cited by 155Health Systems, Economic Evaluations, Quality of LifeComplex Systems and Decision MakingAdaptive Dynamic Programming Control

Modeling Medical Treatment Using Markov Decision Processes

Schaefer, Andrew J., Bailey, Matthew D., Shechter, Steven M., Roberts, Mark S.·2005·International Series in Operations Research & Management Science
DOI 10.1007/1-4020-8066-2_23
Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/1-4020-8066-2_23

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • There is a growing body of evidence that complex treatment decisions are better handled through modeling rather than intuition alone.

    “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]

  • Markov decision processes (MDPs) are one efficient technique for determining optimal sequential decisions in dynamic and uncertain environments.

    “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]

  • The collection of natural history disease data is fraught with many challenges, ethical and otherwise, particularly if untreated conditions pose significant health risks.

    “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]

  • There have been relatively few applications of sequential decision-making techniques like MDPs in healthcare.

    “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]

  • 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.

    “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]

Cites

Flagged first, then by citations

2 of the 53 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Schaefer, A. J., Bailey, M. D., Shechter, S. M., & Roberts, M. S.
Affiliations
University of Pittsburgh (US)
ORCID
Venue
International Series in Operations Research & Management Science
Publisher
Springer US
Published
2005
Pages
593-612
ISSN
0884-8289 (ISSN-L)
DOI
10.1007/1-4020-8066-2_23
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 › Computer Science › Computational Theory and Mathematics › 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 ›

Are you an author, or do you see a problem with this page? Request a review