Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models

Hou, Ping; Jolliet, Olivier; Zhu, Ji; Xu, Ming

doi:10.1016/j.envint.2019.105393

Cited by 86 publications

(57 citation statements)

References 61 publications

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“…Accordingly, it is considered that the number of microbubbles generated was determined by a complex interaction between these four factors. We evaluated the importance of blood viscosity in relation to the number of microbubbles generated using the permutation importance method that can quantify the importance of each input feature by randomly permuting the feature values and by estimating the decrease in the RMSE 28 . The result indicated that random permutation of the blood viscosity worsened the RMSE by 7.3 points, and it was the third most important factor among the factors in microbubble generation after the surgical field suction flow rate and venous reservoir level.…”

Section: Discussionmentioning

confidence: 99%

Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

Miyamoto

Soh

Okahara

et al. 2021

Sci Rep

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The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.

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Section: Discussionmentioning

confidence: 99%

Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

Miyamoto

Soh

Okahara

et al. 2021

Sci Rep

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“…We applied results from such regression techniques to estimate diffusion and material-air partition coefficients used as input for our exposure model (see ESI, Section S4 †). Recent advances in machine learning, such as random forest algorithms or neural networks, offer improved performance compared to pure regression, and were used in our study to estimate ecotoxicity effects 70 and non-cancer human effects. 51 Additional estimation approaches are urgently needed that account for both positive and negative carcinogenicity indications.…”

Section: Discussionmentioning

confidence: 99%

Life cycle based alternatives assessment (LCAA) for chemical substitution

Fantke

Huang

Overcash³

et al. 2020

Green Chem.

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“…CC-BY-NC-ND 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted July 21, 2021. ; https://doi.org/10.1101/2021.07.20.453034 doi: bioRxiv preprint each leave-one-out model and the 2.5% and 97.5% quantile of these predictions are computed and considered as the prediction interval (Hou et al, 2020a).…”

Section: Comparison Proceduresmentioning

confidence: 99%

Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater

Servien

Latrille

Patureau

et al. 2021

Preprint

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It is a real challenge for life cycle assessment practitioners to identify all relevant substances contributing to the ecotoxicity. Once this identification has been made, the lack of corresponding ecotoxicity factors can make the results partial and difficult to interpret. So, it is a real and important challenge to provide ecotoxicity factors for a wide range of compounds. Nevertheless, obtaining such factors using experiments is tedious, time-consuming, and made at a high cost. A modelling method that could predict these factors from easy-to-obtain information on each chemical would be of great value. Here, we present such a method, based on machine learning algorithms, that used molecular descriptors to predict two specific endpoints in continental freshwater for ecotoxicological and human impacts. The different tested machine learning algorithms show good performances on a learning database and the non-linear methods tend to outperform the linear ones. The cluster-then-predict approaches usually show the best performances which suggests that these predicted models must be derived for somewhat similar compounds. Then, predictions were derived from the validated model for compounds with missing toxicity/ecotoxicity factors.

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Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models

Cited by 86 publications

References 61 publications

Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

Life cycle based alternatives assessment (LCAA) for chemical substitution

Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater

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