Abstract
Objective: The accurate evaluation of outcomes at a personalized level in patients with intracerebral hemorrhage (ICH) is critical clinical implications. This study aims to evaluate how machine learning integrates with routine laboratory tests and electronic health records (EHRs) data to predict inpatient mortality after ICH.Methods: In this machine learning-based prognostic study, we included 1,835 consecutive patients with acute ICH between October 2010 and December 2018. The model building process incorporated five pre-implant ICH score variables (clinical features) and 13 out of 59 available routine laboratory parameters. We assessed model performance according to a range of learning metrics, such as the mean area under the receiver operating characteristic curve [AUROC]. We also used the Shapley additive explanation algorithm to explain the prediction model.Results: Machine learning models using laboratory data achieved AUROCs of 0.71–0.82 in a split-by-year development/testing scheme. The non-linear eXtreme Gradient Boosting model yielded the highest prediction accuracy. In the held-out validation set of development cohort, the predictive model using comprehensive clinical and laboratory parameters outperformed those using clinical alone in predicting in-hospital mortality (AUROC [95% bootstrap confidence interval], 0.899 [0.897–0.901] vs. 0.875 [0.872–0.877]; P <0.001), with over 81% accuracy, sensitivity, and specificity. We observed similar performance in the testing set.Conclusions: Machine learning integrated with routine laboratory tests and EHRs could significantly promote the accuracy of inpatient ICH mortality prediction. This multidimensional composite prediction strategy might become an intelligent assistive prediction for ICH risk reclassification and offer an example for precision medicine.
Highlights
To date, spontaneous intracerebral hemorrhage (ICH), a leading cause of stroke and a life-threatening and disabling illness, remains a severe condition worldwide [1,2,3,4]
We introduced the SHapley Additive exPlanation (SHAP) algorithm to help explain the prediction model [22, 23]
With the aforementioned considerations in mind, we initially introduced the SHapley Additive exPlanation (SHAP) algorithm to enhance the robustness of Machine learning (ML) integrated with routine laboratory blood tests to predict inpatient mortality after ICH
Summary
Spontaneous intracerebral hemorrhage (ICH), a leading cause of stroke and a life-threatening and disabling illness, remains a severe condition worldwide [1,2,3,4]. Aggressive care draws more advocates for spontaneous ICH outlined by the current practice guideline, requiring early and accurate identification of individuals who are at risk for unfavorable outcomes [5]. In response to such urgent needs, robust risk estimators are highly recommended. The ICH score [6] is a classic prediction tool widely used in ICH management currently This score assessment system comprises five risk factors: the Glasgow Coma Scale (GCS) score, ICH volume, intraventricular hemorrhage (IVH), the infratentorial origin of ICH, and age simple to use in clinical practice. Such a clinical grading scale is used as a stand-alone risk assessment system and less integrated with additional dimension information, such as routine clinical laboratory profiles, for more comprehensive and precise individual risk assessment
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