Mehrpour Omid, Vohra Varun, Nakhaee Samaneh, Mohtarami Seyed Ali, Shirazi Farshad M
Michigan Poison & Drug Information Center, Wayne State University School of Medicine, Detroit, MI, USA.
Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran.
Sci Rep. 2025 Apr 25;15(1):14468. doi: 10.1038/s41598-025-94395-2.
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.
机器学习算法和人工智能在医学中的应用因其有助于预测医疗结果的能力而引起了广泛关注。本研究旨在评估随机森林算法在预测与急性锂中毒相关的医疗结果方面的有效性。我们分析了2014年1月1日至2018年12月31日期间国家中毒数据系统（NPDS）记录的病例。我们重点关注了年龄在0至89岁之间的急性锂中毒患者。采用随机森林模型来预测严重的医疗结果，包括那些有重大影响、中度影响或死亡的结果。使用预先定义的NPDS编码标准进行预测。通过计算准确率、召回率（敏感性）和F1分数来评估模型的预测性能。在研究期间报告的11525例锂中毒病例中，2760例被归类为急性锂过量。139人经历了严重后果，而2621名患者经历了轻微后果。随机森林模型表现出卓越的准确率和F1分数，训练、验证和测试数据集的相应值分别为99%、98%和98%。对于重要结果，该模型的准确率达到100%，敏感性率达到96%。此外，对于轻微结果，它的准确率为96%，敏感性率为100%。SHapley加法解释（SHAP）研究发现，包括嗜睡/昏睡、年龄、共济失调、腹痛和电解质异常等因素对个体预测有显著影响。随机森林算法在预测急性锂中毒患者的医疗结果方面准确率达到98%。该模型在准确预测重大和轻微结果方面表现出高敏感性和精确性。需要进一步研究来验证这些发现。
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