基于人工智能的脑出血预后预测机床的开发

Girish Menon, Hareesha Ks, Karthik Gajula, Sampath Kumar, Ajay Hegde
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引用次数: 0

摘要

自发性脑出血是卒中的第二常见类型,具有很高的发病率和死亡率。结果预测在这种疾病中非常重要,使我们能够制定治疗策略,特别是在低收入和中等收入国家。今天,预测主要局限于少数临床因素,可能不是很准确。我们探索了一个基于人工智能的平台,结合临床、放射学和生化参数进行结果预测。方法:使用XGBoost机器学习(ML)算法对我们前瞻性维护的脑卒中记录数据进行清理和处理,以使用改进的Rankin量表预测出院时和90天的预后。研究共纳入1000例患者,129个变量在预处理阶段被修剪为19个显著特征。结果:数据集分为9:1,其中900例用于训练,其余100例用于验证。根据平均绝对误差(MAE)对模型进行评估。模型1预测“mRS_Discharge”的MAE为0.34,模型2预测“mRS_3months”的MAE为0.63。结论:ML算法可成功应用于脑出血预后预测。
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Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage
Introduction: Spontaneous intracerebral hemorrhage is the second most common type of stroke with high morbidity and mortality. Outcome prediction is very important in this disease, to enable us tailor treatment strategies especially in a low- and middle-income countries. Today, prediction is predominantly limited to few clinical factors and may not be very accurate. We explore the application of an artificial intelligence-based platform for outcome prediction with a combination of clinical, radiological, and biochemical parameters. Methods: Data from our prospectively maintained stroke register was cleaned and processed using the XGBoost machine learning (ML) algorithm to predict outcome at discharge and 90 days using the modified Rankin scale. A total of 1,000 patients were included in the study, 129 variables were pruned to 19 significant features during the phase of preprocessing. Results: The data set was split 9:1 with 900 cases being used for training and the remaining 100 for validation. The models were evaluated based on the mean absolute error (MAE). Model-1 trained for predicting “mRS_Discharge” had a MAE of 0.34 and model-2 trained for predicting “mRS_3months” had a MAE of 0.63. Conclusion: ML algorithms can be successfully applied for the prediction of outcome in intracerebral hemorrhage.
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