甲醇中毒病人需要插管,如果没有及时预测,会导致无法挽回的并发症甚至死亡.机器学习(ML)和深度学习(DL)等人工智能(AI)技术极大地帮助准确预测甲醇中毒患者的插管需求。所以,我们的研究旨在评估可解释的人工智能(XAI),以预测甲醇中毒患者的插管必要性,比较深度学习和机器学习模型。这项研究分析了来自德黑兰LoghmanHakim医院的897个患者记录的数据集,伊朗,包括甲醇中毒事件,包括需要插管的(202例)和不需要插管的(695例)。八个建立的ML(SVM,XGB,DT,RF)和DL(DNN,FNN,LSTM,使用了CNN)模型。应用了诸如十倍交叉验证和超参数调整之类的技术来防止过拟合。该研究还侧重于通过SHAP和LIME方法的可解释性。基于准确性评估模型性能,特异性,灵敏度,F1分数,和ROC曲线指标。在DL模型中,LSTM在精度方面表现出卓越的性能(94.0%),灵敏度(99.0%),特异性(94.0%),和F1得分(97.0%)。CNN以78.0%的优势领先中华民国。对于ML模型,RF在准确性(97.0%)和特异性(100%)方面表现优异,其次是XGB,灵敏度为99.37%,F1得分(98.27%),和ROC(96.08%)。总的来说,RF和XGB优于其他型号,RF的准确性(97.0%)和特异性(100%),和灵敏度(99.37%),F1得分(98.27%),XGB的ROC(96.08%)。ML模型在所有指标上都超过了DL模型,DL的准确率从93.0%到97.0%,ML的准确率从93.0%到99.0%。DL的敏感性为98.0%至99.37%,ML的敏感性为93.0%至99.0%。DL模型实现了从78.0%到94.0%的特异性,而ML模型范围从93.0%到100%。DL的F1分数在93.0%和97.0%之间,ML在96.0%和98.27%之间。DL模型的ROC评分在68.0%至78.0%之间,而ML模型范围为84.0%至96.08%。预测插管必要性的关键特征包括入院时的GCS,入住ICU,年龄,叶酸治疗持续时间较长,升高的BUN和AST水平,初始记录时的VBG_HCO3,和血液透析的存在。这项研究显示了XAI在预测甲醇中毒患者插管必要性方面的有效性。ML模型,特别是RF和XGB,优于DL同行,强调他们的临床决策潜力。
The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. So, our
study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models. This
study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The
study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This
study as the showcases XAI\'s effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making.