Machine learning for predicting medical outcomes associated with acute lithium poisoning
Abstract The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predicting medical outcomes. This study aimed to evaluate the effectiveness of the random forest algorithm in predicting medical outcomes related to...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-94395-2 |
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| author | Omid Mehrpour Varun Vohra Samaneh Nakhaee Seyed Ali Mohtarami Farshad M. Shirazi |
| author_facet | Omid Mehrpour Varun Vohra Samaneh Nakhaee Seyed Ali Mohtarami Farshad M. Shirazi |
| author_sort | Omid Mehrpour |
| collection | DOAJ |
| description | Abstract The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predicting medical outcomes. This study aimed to evaluate the effectiveness of the random forest algorithm in predicting medical outcomes related to acute lithium toxicity. We analyzed cases recorded in the National Poison Data System (NPDS) between January 1, 2014, and December 31, 2018. We highlighted instances of acute lithium toxicity in patients with ages ranging from 0 to 89 years. A random forest model was employed to predict serious medical outcomes, including those with a major effect, moderate effect, or death. Predictions were made using the pre-defined NPDS coding criteria. The model’s predictive performance was assessed by computing accuracy, recall (sensitivity), and F1-score. Of the 11,525 reported cases of lithium poisoning documented during the study, 2,760 cases were categorized as acute lithium overdose. One hundred thirty-nine individuals experienced severe outcomes, whereas 2,621 patients endured minor outcomes. The random forest model exhibited exceptional accuracy and F1-scores, achieving values of 99%, 98%, and 98% for the training, validation, and test datasets, respectively. The model achieved an accuracy rate of 100% and a sensitivity rate of 96% for important results. In addition, it achieved a 96% accuracy rate and a sensitivity rate of 100% for minor outcomes. The SHapley Additive exPlanations (SHAP) study found factors, including drowsiness/lethargy, age, ataxia, abdominal pain, and electrolyte abnormalities, significantly influenced individual predictions. The random forest algorithm achieved a 98% accuracy rate in predicting medical outcomes for patients with acute lithium intoxication. The model demonstrated high sensitivity and precision in accurately predicting significant and minor outcomes. Further investigation is necessary to authenticate these findings. |
| format | Article |
| id | doaj-art-ac2e0cd8038a4d4782ea5181c8d5ab18 |
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| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ac2e0cd8038a4d4782ea5181c8d5ab182025-08-20T02:19:57ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-94395-2Machine learning for predicting medical outcomes associated with acute lithium poisoningOmid Mehrpour0Varun Vohra1Samaneh Nakhaee2Seyed Ali Mohtarami3Farshad M. Shirazi4Michigan Poison & Drug Information Center, Wayne State University School of MedicineMichigan Poison & Drug Information Center, Wayne State University School of MedicineMedical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS)Department of Computer Engineering and Information Technology, Payame Noor University (PNU)Arizona Poison and Drug Information Center, University of ArizonaAbstract The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predicting medical outcomes. This study aimed to evaluate the effectiveness of the random forest algorithm in predicting medical outcomes related to acute lithium toxicity. We analyzed cases recorded in the National Poison Data System (NPDS) between January 1, 2014, and December 31, 2018. We highlighted instances of acute lithium toxicity in patients with ages ranging from 0 to 89 years. A random forest model was employed to predict serious medical outcomes, including those with a major effect, moderate effect, or death. Predictions were made using the pre-defined NPDS coding criteria. The model’s predictive performance was assessed by computing accuracy, recall (sensitivity), and F1-score. Of the 11,525 reported cases of lithium poisoning documented during the study, 2,760 cases were categorized as acute lithium overdose. One hundred thirty-nine individuals experienced severe outcomes, whereas 2,621 patients endured minor outcomes. The random forest model exhibited exceptional accuracy and F1-scores, achieving values of 99%, 98%, and 98% for the training, validation, and test datasets, respectively. The model achieved an accuracy rate of 100% and a sensitivity rate of 96% for important results. In addition, it achieved a 96% accuracy rate and a sensitivity rate of 100% for minor outcomes. The SHapley Additive exPlanations (SHAP) study found factors, including drowsiness/lethargy, age, ataxia, abdominal pain, and electrolyte abnormalities, significantly influenced individual predictions. The random forest algorithm achieved a 98% accuracy rate in predicting medical outcomes for patients with acute lithium intoxication. The model demonstrated high sensitivity and precision in accurately predicting significant and minor outcomes. Further investigation is necessary to authenticate these findings.https://doi.org/10.1038/s41598-025-94395-2LithiumPoisoningMachine learningArtificial intelligence |
| spellingShingle | Omid Mehrpour Varun Vohra Samaneh Nakhaee Seyed Ali Mohtarami Farshad M. Shirazi Machine learning for predicting medical outcomes associated with acute lithium poisoning Scientific Reports Lithium Poisoning Machine learning Artificial intelligence |
| title | Machine learning for predicting medical outcomes associated with acute lithium poisoning |
| title_full | Machine learning for predicting medical outcomes associated with acute lithium poisoning |
| title_fullStr | Machine learning for predicting medical outcomes associated with acute lithium poisoning |
| title_full_unstemmed | Machine learning for predicting medical outcomes associated with acute lithium poisoning |
| title_short | Machine learning for predicting medical outcomes associated with acute lithium poisoning |
| title_sort | machine learning for predicting medical outcomes associated with acute lithium poisoning |
| topic | Lithium Poisoning Machine learning Artificial intelligence |
| url | https://doi.org/10.1038/s41598-025-94395-2 |
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