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. 2024 Jun 20;29(1):341.
doi: 10.1186/s40001-024-01940-2.

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

Lingyu Xu #  1 , Chenyu Li #  1   2 , Jiaqi Zhang  3 , Chen Guan  1 , Long Zhao  1 , Xuefei Shen  1 , Ningxin Zhang  1 , Tianyang Li  1 , Chengyu Yang  1 , Bin Zhou  1 , Quandong Bu  1 , Yan Xu  4
Affiliations

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

Lingyu Xu et al. Eur J Med Res. .

Abstract

Background: Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality.

Methods: We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction.

Results: The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk.

Conclusions: Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

Keywords: Acute ischemic stroke; Acute kidney disease; Artificial intelligence; Machine learning; Mortality.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Architectural diagram of study
Fig. 2
Fig. 2
The SHAP summary plots for LightGBM models and force plots for two representative patients. A The ranking of feature importance within the mortality prediction model. Features with higher mean absolute SHAP values signify increased predictive influence. B Each dot represents the SHAP value of a specific feature for an individual, with red and blue indicating high and low feature values, respectively. On the x-axis, a positive or negative SHAP value signifies that the feature positively or negatively influenced the AKD prediction for the individual. C provides a personalized explanation for a case with a mortality probability below 10% and an actual outcome of survival. Features are ranked from the center to both ends based on the extent of their impact. The impact of a feature on the model’s output is directly proportional to the size of the arrow. The positive impact of a feature is depicted in red, elevating the prediction from the base value, while the negative effect is shown in blue, lowering the prediction. Certain features, such as Scr (107 μmol/L) and TBIL (13.6 μmol/L), exhibit a positive influence, while the absence of ACEI/ARB, diuretics, and antibiotics, as well as the absence of kidney disease, contribute negatively to predicting mortality. D provides a personalized explanation for a case with a mortality probability exceeding 90% and an actual outcome of mortality. The base value represents the averaged predicted results
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