Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction
Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding infants. This study introduces an explainable machi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325000430 |
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| author | Xiaojie Huang Jiajia Chen Peineng Liu |
| author_facet | Xiaojie Huang Jiajia Chen Peineng Liu |
| author_sort | Xiaojie Huang |
| collection | DOAJ |
| description | Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding infants. This study introduces an explainable machine learning model to predict the risk of chemical transfer through human milk. Our novel framework integrates ensemble resampling methods with advanced feature selection techniques, addressing data imbalance and enhancing predictive accuracy. The balanced random forest classifier, optimized using the genetic algorithm for feature selection, achieved an area under the receiver operating characteristic curve (AUC) of 0.8708 and an accuracy of 82.67 % on the internal test set, with an accuracy of 86.36 % on the external validation set. The integration of the SHapley Additive exPlanations approach provided deeper insights by revealing how specific chemical properties influence the transfer of high-risk compounds into breast milk. This enhanced interpretability offers a clearer understanding of the associated risks and informs strategies for their mitigation. Structural alert analysis further identified molecular fragments linked to high-risk chemicals, enabling targeted risk assessments. Additionally, the model was applied to evaluate the transfer risks of FDA-approved drugs from 2019 to 2024, identifying several with high transfer probabilities. To broaden its application, we developed an online prediction tool that offers real-time risk assessments, providing an accessible resource for healthcare professionals and researchers. These contributions present a robust, ethically sound tool for assessing chemical exposure risks in breastfeeding infants, supporting informed decisions on drug use and environmental contaminant exposure. |
| format | Article |
| id | doaj-art-95a9478fabe9449cb67f06a83e621a47 |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-95a9478fabe9449cb67f06a83e621a472025-01-23T05:26:11ZengElsevierEcotoxicology and Environmental Safety0147-65132025-01-01289117707Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer predictionXiaojie Huang0Jiajia Chen1Peineng Liu2Corresponding author.; Department of Pharmacy, Jieyang People’s Hospital, Jieyang, ChinaDepartment of Pharmacy, Jieyang People’s Hospital, Jieyang, ChinaDepartment of Pharmacy, Jieyang People’s Hospital, Jieyang, ChinaBreast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding infants. This study introduces an explainable machine learning model to predict the risk of chemical transfer through human milk. Our novel framework integrates ensemble resampling methods with advanced feature selection techniques, addressing data imbalance and enhancing predictive accuracy. The balanced random forest classifier, optimized using the genetic algorithm for feature selection, achieved an area under the receiver operating characteristic curve (AUC) of 0.8708 and an accuracy of 82.67 % on the internal test set, with an accuracy of 86.36 % on the external validation set. The integration of the SHapley Additive exPlanations approach provided deeper insights by revealing how specific chemical properties influence the transfer of high-risk compounds into breast milk. This enhanced interpretability offers a clearer understanding of the associated risks and informs strategies for their mitigation. Structural alert analysis further identified molecular fragments linked to high-risk chemicals, enabling targeted risk assessments. Additionally, the model was applied to evaluate the transfer risks of FDA-approved drugs from 2019 to 2024, identifying several with high transfer probabilities. To broaden its application, we developed an online prediction tool that offers real-time risk assessments, providing an accessible resource for healthcare professionals and researchers. These contributions present a robust, ethically sound tool for assessing chemical exposure risks in breastfeeding infants, supporting informed decisions on drug use and environmental contaminant exposure.http://www.sciencedirect.com/science/article/pii/S0147651325000430Chemical exposure riskBreastfeeding infantsExplainable machine learningEnsemble resampling methodsSHapley Additive exPlanationsStructural alert |
| spellingShingle | Xiaojie Huang Jiajia Chen Peineng Liu Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction Ecotoxicology and Environmental Safety Chemical exposure risk Breastfeeding infants Explainable machine learning Ensemble resampling methods SHapley Additive exPlanations Structural alert |
| title | Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction |
| title_full | Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction |
| title_fullStr | Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction |
| title_full_unstemmed | Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction |
| title_short | Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction |
| title_sort | assessing chemical exposure risk in breastfeeding infants an explainable machine learning model for human milk transfer prediction |
| topic | Chemical exposure risk Breastfeeding infants Explainable machine learning Ensemble resampling methods SHapley Additive exPlanations Structural alert |
| url | http://www.sciencedirect.com/science/article/pii/S0147651325000430 |
| work_keys_str_mv | AT xiaojiehuang assessingchemicalexposureriskinbreastfeedinginfantsanexplainablemachinelearningmodelforhumanmilktransferprediction AT jiajiachen assessingchemicalexposureriskinbreastfeedinginfantsanexplainablemachinelearningmodelforhumanmilktransferprediction AT peinengliu assessingchemicalexposureriskinbreastfeedinginfantsanexplainablemachinelearningmodelforhumanmilktransferprediction |