A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support

Qiu X, Tan X, Li Q, Chen S, Ru Y, Jin Y (2022)
Knowledge-Based Systems 237: 107689.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Qiu, Xihe; Tan, Xiaoyu; Li, Qiong; Chen, Shaotao; Ru, Yajun; Jin, YaochuUniBi
Abstract / Bemerkung
Precise prescription of medication dosing is crucial to patients, especially among Intensive Care Unit (ICU) patients. However, improper administration of some sensitive therapeutic medications (e.g., heparin) might place patients at unneeded risk, even life-threatening. Numerous factors such as a patient's clinical phenotype, genotype, and environmental factors will affect the heparin dose response. As a result, it is challenging to prescribe the optimal initial dose of heparin. In this paper, an individualized dosing policy is proposed to determine the optimal initial dose and minimize the risk of mis-dosing, as well as preventing the patients from late complications associated with medications dosing. A latent batch-constrained deep reinforcement learning (RL) algorithm is proposed to guarantee the safety of the medication recommendation system. The agent can observe a latent representation of patents and generate medication dosing solutions in successive and limited action spaces. The individualized dosing policy aims to reduce the extrapolation errors in the off-policy algorithms, by evaluating over-dosing and under-dosing of heparin in patients. Our results evaluated on Medical Information Mart for Intensive Care III (MIMIC-III) database demonstrate that the latent batch-constrained RL algorithm can work effectively from the retrospective data, showing promise to be used in future medication dosing policies.(C)& nbsp;2021 Elsevier B.V. All rights reserved.
Stichworte
Medical decision support system; Artificial intelligence; Deep; reinforcement learning; Machine learning
Erscheinungsjahr
2022
Zeitschriftentitel
Knowledge-Based Systems
Band
237
Art.-Nr.
107689
ISSN
0950-7051
eISSN
1872-7409
Page URI
https://pub.uni-bielefeld.de/record/2963021

Zitieren

Qiu X, Tan X, Li Q, Chen S, Ru Y, Jin Y. A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support. Knowledge-Based Systems . 2022;237: 107689.
Qiu, X., Tan, X., Li, Q., Chen, S., Ru, Y., & Jin, Y. (2022). A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support. Knowledge-Based Systems , 237, 107689. https://doi.org/10.1016/j.knosys.2021.107689
Qiu, X., Tan, X., Li, Q., Chen, S., Ru, Y., and Jin, Y. (2022). A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support. Knowledge-Based Systems 237:107689.
Qiu, X., et al., 2022. A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support. Knowledge-Based Systems , 237: 107689.
X. Qiu, et al., “A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support”, Knowledge-Based Systems , vol. 237, 2022, : 107689.
Qiu, X., Tan, X., Li, Q., Chen, S., Ru, Y., Jin, Y.: A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support. Knowledge-Based Systems . 237, : 107689 (2022).
Qiu, Xihe, Tan, Xiaoyu, Li, Qiong, Chen, Shaotao, Ru, Yajun, and Jin, Yaochu. “A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support”. Knowledge-Based Systems 237 (2022): 107689.

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Suchen in

Google Scholar