Medical resource allocation planning by integrating machine learning and optimization models

Mizan, Tasquia; Taghipour, Sharareh

doi:10.1016/j.artmed.2022.102430

Cited by 11 publications

(7 citation statements)

References 48 publications

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“…It allows analyzing different scenarios and percentages of use based on objectives clearly set by the management. Other tools, such as the integration of machine learning models and linear programming [40] and Markovian modeling [41], that allow resource planning in the hospital context are evidenced in the literature.…”

Section: Verification Of Improvements Introducedmentioning

confidence: 99%

Discrete simulation applied to the improvement of patient flows in hospital institutions

Sánchez Suárez,

Hernández Nariño,

Marqués León

et al. 2024

Ingeniare. Rev. chil. ing.

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Hospital services need to develop management based on the phenomena that make up their environment and project themselves to the needs of their patients. In this sense, managing patient flows allows for optimizing hospital resources and increasing patient safety during the patient's journey. The objective of this article is to improve the management of patient flows in hospital institutions through the application of discrete simulation. The proposed methodology integrates the processes of planning, execution, verification, and action. Among the tools used are the process descriptions, Diagnosis Related Groups definition, and discrete simulation. Additionally, tools from manufacturing systems are adapted to hospital management in the solutions proposal. Among the main results, patient flow management is improved in the Urology service of the Faustino Pérez Hernández Clinical and Surgical Teaching Hospital in Matanzas province, Cuba. The process is described, and a simulation model is built to identify insufficiencies and check the feasibility of improvements. Discrete simulation is an operations management tool that facilitates managing and improving patient flows in hospital institutions.

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Section: Verification Of Improvements Introducedmentioning

confidence: 99%

Discrete simulation applied to the improvement of patient flows in hospital institutions

Sánchez Suárez,

Hernández Nariño,

Marqués León

et al. 2024

Ingeniare. Rev. chil. ing.

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“…doctor visits, and medical procedures -based on their disease history and other pertinent factors. Such prediction models are key to efficient management and planning in healthcare [63], [49], and are for example already employed in numerous countries to allocate healthcare resources [45]. One significant challenge lies in making accurate predictions for patients belonging to divergent subgroups.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Forecasting in Electronic Health Records With Attention-Free Models

Kumar,

Ilin,

Salo

et al. 2024

IEEE Trans. Artif. Intell.

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Despite the proven effectiveness of Transformer neural networks across multiple domains, their performance with Electronic Health Records (EHR) can be nuanced. The unique, multidimensional sequential nature of EHR data can sometimes make even simple linear models with carefully engineered features more competitive. Thus, the advantages of Transformers, such as efficient transfer learning and improved scalability are not always fully exploited in EHR applications.In this work, we aim to forecast the demand for healthcare services, by predicting the number of patient visits to healthcare facilities. The challenge amplifies when dealing with divergent patient subgroups, like those with rare diseases, which are characterized by unique health trajectories and are typically smaller in size. To address this, we employ a self-supervised pretraining strategy, Generative Summary Pretraining (GSP), which predicts future summary statistics based on past health records of a patient. Our models are pretrained on a health registry of nearly one million patients, then fine-tuned for specific subgroup prediction tasks, showcasing the potential to handle the multifaceted nature of EHR data.In evaluation, SANSformer consistently surpasses robust EHR baselines, with our GSP pretraining method notably amplifying model performance, particularly within smaller patient subgroups. Our results illuminate the promising potential of tailored attention-free models and self-supervised pretraining in refining healthcare utilization predictions across various patient demographics.Impact Statement-Large neural networks have demonstrated success in various predictive tasks using Electronic Health Records (EHR). However, their performance in small divergent patient cohorts, such as those with rare diseases, often falls short of simpler linear models due to the substantial data requirements of large models. To address this limitation, we introduce the SANSformers architecture, specifically designed for forecasting healthcare utilization within EHR. Distinct from traditional transformers, SANSformers utilize attention-free mechanisms, thereby reducing complexity. We also present Generative Summary Pretraining (GSP), a self-supervised learning technique that enables large neural networks to maintain predictive efficiency even with smaller patient subgroups. Through extensive evaluation across two real-world datasets, we provide a comparative analysis with existing state-of-the-art EHR prediction models, offering a new perspective on predicting healthcare utilization.

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“…The calculation of DPKs can be considered a multi-target regression problem in which from the characteristics of the medium, such as chemical composition and density, as well as physical characteristics, such as the initial energy of the source electrons and the range of these in the medium considered, the DPK value as a function of the distance r to the source is predicted as a target variable [ 35 ]. These types of models are widely used in problems of ecological modeling [ 36 ], healthcare [ 37 ], environmental [ 38 ], and drug discovery [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based model for a dose point kernel calculation

2023

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Purpose Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters. Methods DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with $$^{90}$$ 90 Y. Results The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than $$10\%$$ 10 % in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than $$7 \%$$ 7 % were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations. Conclusion An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.

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Medical resource allocation planning by integrating machine learning and optimization models

Cited by 11 publications

References 48 publications

Discrete simulation applied to the improvement of patient flows in hospital institutions

Discrete simulation applied to the improvement of patient flows in hospital institutions

Self-Supervised Forecasting in Electronic Health Records With Attention-Free Models

A machine learning-based model for a dose point kernel calculation

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