Al Alawi Abdullah M, Al Kaabi Hoor, Al Falahi Zubaida, Al-Naamani Zakariya, Al Busafi Said
General Medicine Unit, Department of Medicine, Sultan Qaboos University Hospital, Muscat, Oman.
Internal Medicine Training Program, Oman Medical Specialty Board, Muscat, Oman.
Oman Med J. 2024 May 30;39(3):e632. doi: 10.5001/omj.2024.79. eCollection 2024 May.
Chronic liver disease and cirrhosis are persistent global health threats, ranking among the top causes of death. Despite medical advancements, their mortality rates have remained stagnant for decades. Existing scoring systems such as Child-Turcotte-Pugh and Mayo End-Stage Liver Disease have limitations, prompting the exploration of more accurate predictive methods using artificial intelligence and machine learning (ML).
We retrospectively reviewed the data of all adult patients with acute decompensated liver cirrhosis admitted to a tertiary hospital during 2015-2021. The dataset underwent preprocessing to handle missing values and standardize continuous features. Traditional ML and deep learning algorithms were applied to build a 28-day mortality prediction model.
The subjects were 173 cirrhosis patients, whose medical records were examined. We developed and evaluated multiple models for 28-day mortality prediction. Among traditional ML algorithms, logistic regression outperformed was achieving an accuracy of 82.9%, precision of 55.6%, recall of 71.4%, and an F1-score of 0.625. Naive Bayes and Random Forest models also performed well, both achieving the same accuracy (82.9%) and precision (54.5%). The deep learning models (multilayer artificial neural network, recurrent neural network, and Long Short-Term Memory) exhibited mixed results, with the multilayer artificial neural network achieving an accuracy of 74.3% but lower precision and recall. The feature importance analysis identified key predictability contributors, including admission in the intensive care unit (importance: 0.112), use of mechanical ventilation (importance: 0.095), and mean arterial pressure (importance: 0.073).
Our study demonstrates the potential of ML in predicting 28-day mortality following hospitalization with acute decompensation of liver cirrhosis. Logistic Regression, Naive Bayes, and Random Forest models proved effective, while deep learning models exhibited variable performance. These models can serve as useful tools for risk stratification and timely intervention. Implementing these models in clinical practice has the potential to improve patient outcomes and resource allocation.
慢性肝病和肝硬化是持续存在的全球健康威胁，位列主要死因。尽管医学不断进步，但数十年来其死亡率一直停滞不前。现有的评分系统，如Child-Turcotte-Pugh评分和梅奥终末期肝病评分存在局限性，这促使人们探索使用人工智能和机器学习（ML）的更准确预测方法。
我们回顾性分析了2015年至2021年期间在一家三级医院住院的所有成年急性失代偿期肝硬化患者的数据。对数据集进行预处理以处理缺失值并标准化连续特征。应用传统机器学习和深度学习算法构建28天死亡率预测模型。
研究对象为173例肝硬化患者，对其病历进行了检查。我们开发并评估了多个28天死亡率预测模型。在传统机器学习算法中，逻辑回归表现最佳，准确率达82.9%，精确率为55.6%，召回率为71.4%，F1值为0.625。朴素贝叶斯和随机森林模型也表现良好，准确率（82.9%）和精确率（54.5%）相同。深度学习模型（多层人工神经网络、循环神经网络和长短期记忆网络）结果不一，多层人工神经网络准确率为74.3%，但精确率和召回率较低。特征重要性分析确定了关键的预测因素，包括入住重症监护病房（重要性：0.112）、使用机械通气（重要性：0.095）和平均动脉压（重要性：0.073）。
我们的研究证明了机器学习在预测肝硬化急性失代偿住院后28天死亡率方面的潜力。逻辑回归、朴素贝叶斯和随机森林模型被证明是有效的，而深度学习模型表现各异。这些模型可作为风险分层和及时干预的有用工具。在临床实践中应用这些模型有可能改善患者预后和资源分配。
