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. 2023 Jan 19;23(3):1162.
doi: 10.3390/s23031162.

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques

José A González-Nóvoa  1 , Laura Busto  1 , Silvia Campanioni  1 , José Fariña  2 , Juan J Rodríguez-Andina  2 , Dolores Vila  3 , César Veiga  1
Affiliations

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques

José A González-Nóvoa et al. Sensors (Basel). .

Abstract

Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to select appropriate treatment options and to predict patients' conditions. There has been a high amount of data collected by biomedical sensors during the continuous monitoring process of patients in the ICU, so the use of artificial intelligence techniques in automatic LoS estimation would improve patients' care and facilitate the work of healthcare personnel. In this work, a novel methodology to estimate the LoS using data of the first 24 h in the ICU is presented. To achieve this, XGBoost, one of the most popular and efficient state-of-the-art algorithms, is used as an estimator model, and its performance is optimized both from computational and precision viewpoints using Bayesian techniques. For this optimization, a novel two-step approach is presented. The methodology was carefully designed to execute codes on a high-performance computing system based on graphics processing units, which considerably reduces the execution time. The algorithm scalability is analyzed. With the proposed methodology, the best set of XGBoost hyperparameters are identified, estimating LoS with a MAE of 2.529 days, improving the results reported in the current state of the art and probing the validity and utility of the proposed approach.

Keywords: Bayesian optimization; ICU occupancy; MIMIC; XGBoost; artificial intelligence; automated machine learning; intensive care unit; length of stay; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest. The founders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

Figures

Figure 1
Figure 1
Intensive care unit structure.
Figure 2
Figure 2
Outline of the proposed methodology. (a) Shows a general outline, while a more detailed explanation of the hyperparameter search using Bayesian optimization is shown in (b).
Figure 3
Figure 3
Evolution of minimum MAE across 50 iteration intervals.
Figure 4
Figure 4
Evolution of maximum depth value and its corresponding MAE across the trials.
See this image and copyright information in PMC

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