Data-driven hospitals staff and resources allocation using agent-based simulation and deep reinforcement learning

Lazebnik, Teddy

doi:10.1016/j.engappai.2023.106783

Cited by 13 publications

(7 citation statements)

References 62 publications

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“…This study is another contribution to the growing field of computer-aided solutions for “soft” questions using data-driven based methods 57 – 61 . To the best of our knowledge, this study is the first to provide a machine-learning model for objectively scoring a strictly controlled dog behavioral test.…”

Section: Discussionmentioning

confidence: 99%

Digitally-enhanced dog behavioral testing

Farhat,

Lazebnik,

Monteny

et al. 2023

Sci Rep

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Behavioral traits in dogs are assessed for a wide range of purposes such as determining selection for breeding, chance of being adopted or prediction of working aptitude. Most methods for assessing behavioral traits are questionnaire or observation-based, requiring significant amounts of time, effort and expertise. In addition, these methods might be also susceptible to subjectivity and bias, negatively impacting their reliability. In this study, we proposed an automated computational approach that may provide a more objective, robust and resource-efficient alternative to current solutions. Using part of a ‘Stranger Test’ protocol, we tested n = 53 dogs for their response to the presence and neutral actions of a stranger. Dog coping styles were scored by three dog behavior experts. Moreover, data were collected from their owners/trainers using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ). An unsupervised clustering of the dogs’ trajectories revealed two main clusters showing a significant difference in the stranger-directed fear C-BARQ category, as well as a good separation between (sufficiently) relaxed dogs and dogs with excessive behaviors towards strangers based on expert scoring. Based on the clustering, we obtained a machine learning classifier for expert scoring of coping styles towards strangers, which reached an accuracy of 78%. We also obtained a regression model predicting C-BARQ scores with varying performance, the best being Owner-Directed Aggression (with a mean average error of 0.108) and Excitability (with a mean square error of 0.032). This case study demonstrates a novel paradigm of ‘machine-based’ dog behavioral assessment, highlighting the value and great promise of AI in this context.

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Section: Discussionmentioning

confidence: 99%

Digitally-enhanced dog behavioral testing

Farhat,

Lazebnik,

Monteny

et al. 2023

Sci Rep

Self Cite

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“…To address resource allocation challenges within hospitals, optimization models have been proposed ( 57 ). Lazebnik ( 21 ), proposed a deep reinforcement learning-based model that uses agent-based simulation with limited historical data to suggest stuff and recourse allocation policies for a wide range of objectives. The model considered factors such as patient demand, resource availability, and treatment priorities, resulting in an optimal allocation of beds, staff, and medical supplies to improve patient care.…”

Section: Related Workmentioning

confidence: 99%

“…We shall refer to it as the HSP's policy . For the purpose of realizing a realistic HSPs' requirement to treat a patient population, we used the data and synthetic data simulator proposed by Lazebnik ( 21 ) which is based on real-world data from four community HSPs that includes the patient population sizes, their illness distribution, obtained treatments, and estimation to the overall OEB.…”

Section: In-silico Analysismentioning

confidence: 99%

“…Indeed, there is a growing body of literature about the efficient usage of resources in the healthcare sphere, ranging from a single patient to an entire hospital ( 21 – 28 ). However, economic evaluations of personalized medicine's economic impact have been mostly neglected.…”

Section: Introductionmentioning

confidence: 99%

“…One way to tackle this challenge is using an economical-mathematical model together with data science methods, as these have shown to be powerful tools in similar tasks ( 29 – 32 ). In practice, data science has emerged as a powerful tool in healthcare, revolutionizing the way medical decisions are made and HSPs are managed ( 21 , 33 , 34 ). Data-driven approaches allow hospitals to identify subpopulations that would benefit the most from personalized medicine, thus addressing the challenge of resource scarcity ( 35 – 37 ).…”

Section: Introductionmentioning

confidence: 99%

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Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

Savchenko,

Bunimovich-Mendrazitsky

2024

Front. Med.

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In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

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Yingngam

2024

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Data-driven hospitals staff and resources allocation using agent-based simulation and deep reinforcement learning

Cited by 13 publications

References 62 publications

Digitally-enhanced dog behavioral testing

Digitally-enhanced dog behavioral testing

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

Machine Learning Applications for Drug Repurposing

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