Abstract
Purpose
The existing sepsis treatment lacks effective reference and relies too much on the experience of clinicians. Therefore, we used the reinforcement learning model to build an assisted model for the sepsis medication treatment.
Methods
Using the latest Sepsis 3.0 diagnostic criteria, 19,582 sepsis patients were screened from the Medical Intensive Care Information III database for treatment strategy research, and forty-six features were used in modeling. The study object of the medication strategy is the dosage of vasopressor drugs and intravenous infusion. Dueling DDQN is proposed to predict the patient’s medication strategy (vasopressor and intravenous infusion dosage) through the relationship between the patient’s state, reward function, and medication action. We also constructed protection against the possible high-risk behaviors of Dueling DDQN, especially sudden dose changes of vasopressors can lead to harmful clinical effects. In order to improve the guiding effect of clinically effective medication strategies on the model, we proposed a hybrid model (safe-dueling DDQN + expert strategies) to optimize medication strategies.
Results
The Dueling DDQN medication model for sepsis patients is superior to clinical strategies and other models in terms of off-policy evaluation values and mortality, and reduced the mortality of clinical strategies from 16.8 to 13.8%. Safe-Dueling DDQN we proposed, compared with Dueling DDQN, has an overall reduction in actions involving vasopressors and reduces large dose fluctuations. The hybrid model we proposed can switch between expert strategies and safe dueling DDQN strategies based on the current state of patients.
Conclusions
The reinforcement learning model we proposed for sepsis medication treatment, has practical clinical value and can improve the survival rate of patients to a certain extent while ensuring the balance and safety of medication.
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References
Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, Rubenfeld G, Kahn JM, Shankar-Hari M, Singer M, Deutschman CS. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315(8):762–74.
Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, Kumar A, Sevransky JE, Sprung CL, Nunnally ME, Rochwerg B. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensiv Care Med. 2017;43:304–77. https://doi.org/10.1007/s00134-017-4683-6.
Gaieski DF, Edwards JM, Kallan MJ, et al. Benchmarking the incidence and mortality of severe sepsis in the United States. Crit Care Med. 2013. https://doi.org/10.1097/CCM.0b013e31827c09f8.
Levy MM, Evans LE, Rhodes A. The surviving sepsis campaign bundle: 2018 update. Intensiv Care Med. 2018. https://doi.org/10.1007/s00134-018-5085-0.
Jinxin Z, Kuo S, Dahai H, et al. (2022) Advances in early diagnosis and treatment of sepsis. Chinese journal of injury and repair (Electronic Edition)
Littman M. Reinforcement learning improves behaviour from evaluative feedback. Nature. 2015. https://doi.org/10.1038/nature14540.
Jeter R, Josef C, Shashikumar S, Nemati S. (2019) Does the “Artificial Intelligence Clinician” learn optimal treatment strategies for sepsis in intensive care? arXiv preprint arXiv: 1902.03271. https://arxiv.org/abs/1902.03271
Johnson A, Pollard T, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016. https://doi.org/10.1038/sdata.2016.35.
Van Hasselt H, Guez A, Silver D. (2016) Deep reinforcement learning with double Q-Learning. National Conference on Artificial Intelligence, Beijing, China: IEEE. https://doi.org/10.1609/aaai.v30i1.10295.
Wang G, Schaul T, Hessel M, et al. (2016) Dueling network architectures for deep reinforcement learning. International Conference on Machine Learning, USA: IEEE. http://proceedings.mlr.press/v48/wangf16.pdf.
Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016. https://doi.org/10.1001/jama.2016.0287.
Raghu A, Komorowski M, Celi L A, Szolovits P, Ghassemi M. (2017) Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach. Machine Learning for Healthcare Conference. https://proceedings.mlr.press/v68/raghu17a.html.
Peng X, Ding Y, Wihl D, Gottesman O, Komorowski M, Li-wei HL, Ross A, Faisal A, Doshi-Velez F. (2018) Improving sepsis treatment strategies by combining deep and kernel-based reinforcement learning. American Medical Informatics Association (AMIA) Annual Symposium Proceedings. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371300/.
Futoma, J, Lin, A, Sendak, M, Bedoya, A, Clement, M, O’Brien, C, Heller, K. (2018) Learning to treat sepsis with multi-output gaussian process deep recurrent q-networks. https://openreview.net/forum?id=SyxCqGbRZ.
Roggeveen L, El Hassouni A, Ahrendt J, Guo T, Fleuren L, Thoral P, Girbes AR, Hoogendoorn M, Elbers PW. Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis. Artif Intell Med. 2021. https://doi.org/10.1016/j.artmed.2020.102003.
Fohner AE, Greene JD, Lawson BL, Chen JH, Kipnis P, Escobar GJ, Liu VX. Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning. J Am Med Inform Assoc. 2019;26(12):1466–77. https://doi.org/10.1093/jamia/ocz161.
Vincent JL, de Backer D. Circulatory shock. N Engl J Med. 2013. https://doi.org/10.1056/NEJMra1208943.
Malbrain ML, Van Regenmortel N, Saugel B, De Tavernier B, Van Gaal PJ, Joannes-Boyau O, Teboul JL, Rice TW, Mythen M, Monnet X. Principles of fluid management and stewardship in septic shock: it is time to consider the four D’s and the four phases of fluid therapy. Ann Intensiv Care. 2018. https://doi.org/10.1186/s13613-018-0402-x.
Kotani Y, Di Gioia A, Landoni G, Belletti A, Khanna AK. An updated “norepinephrine equivalent” score in intensive care as a marker of shock severity. Crit Care. 2023. https://doi.org/10.1186/s13054-023-04322-y.
Jia Y, Lawton T, Burden J, Burden J, McDermid J, Habli I. Safety-driven design of machine learning for sepsis treatment. J Biomed Inform. 2021. https://doi.org/10.1016/j.jbi.2021.103762.
Liang D, Deng H, Liu Y. The treatment of sepsis: an episodic memory-assisted deep reinforcement learning approach. Appl Intell. 2022. https://doi.org/10.1007/s10489-022-04099-7.
Tianhao L, Zhishun W, Wei L, Zhang Q. Electronic health records based reinforcement learning for treatment optimizing. Inf Syst. 2022. https://doi.org/10.1016/j.is.2021.101878.
Jia, Yan, et al. (2020) "Safe reinforcement learning for sepsis treatment." 2020 IEEE International conference on healthcare informatics (ICHI). IEEE. https://doi.org/10.1109/ICHI48887.2020.9374403.
Fatemi M, Killian TW, Subramanian J, Ghassemi M. (2021) Medical dead-ends and learning to identify high-risk states and treatments. Adv Neural Inf Proces Syst. https://proceedings.neurips.cc/paper_files/paper/2021/hash/26405399c51ad7b13b504e74eb7c696c-Abstract.html.
Chan A J, van der Schaar M. (2021) Scalable Bayesian inverse reinforcement learning. International Conference on Learning Representations.https://doi.org/10.48550/arXiv.2102.06483.
Liu X, Yu C, Huang Q, Wang L, Wu J, Guan X. (2021) Combining Model-Based and Model-Free Reinforcement Learning Policies for More Efficient Sepsis Treatment. In Bioinformatics Research and Applications: 17th International Symposium, ISBRA. https://doi.org/10.1007/978-3-030-91415-8_10.
Beier K, Eppanapally S, Bazick HS, Chang D, Mahadevappa K, Gibbons FK, Christopher KB. Elevation of bun is predictive of long-term mortality in critically ill patients independent of normal creatinine. Crit Care Med. 2011;39(2):305.
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We thank all authors for their contributions to this work.
Funding
This work was supported by the Academic Leader Program of Shanghai Public Health System Construction 3-Year Action Plan (2020–2022) (Grant Number: GWV-10.2-XD32); Shanghai “Science and Technology Innovation Action Plan” Biomedical Science and Technology Support Special Project (Grant Number: 20S31905100); Shanghai Engineering Technology Research Center Support Project (Grant Number: 18DZ2250900).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by TZ, DW and MZ. The first draft of the manuscript was written by TZ and all authors commented on previous versions of the manuscript. TZ, YQ and MZ completed the revisions of the manuscript. All authors read and approved the final manuscript.
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Zhang, T., Qu, Y., wang, D. et al. Optimizing sepsis treatment strategies via a reinforcement learning model. Biomed. Eng. Lett. 14, 279–289 (2024). https://doi.org/10.1007/s13534-023-00343-2
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DOI: https://doi.org/10.1007/s13534-023-00343-2