Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP

Wang, Ke; Tian, Jing; Zheng, Chen; Yang, Hong; Ren, Jia; Liu, Yanling; Han, Qinghua; Zhang, Yanbo

doi:10.1016/j.compbiomed.2021.104813

Cited by 98 publications

(50 citation statements)

References 31 publications

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“…Lundberg et al developed the SHAP model, which uses the SHAP value as a uniform measure of the importance of features used in the machine learning models ( Lundberg et al, 2020 ). By attributing output values to the Shapley value of each feature, researchers have performed interpretability analysis of machine learning models ( Wang et al, 2021 ; Wojtuch et al, 2021 ; Scavuzzo et al, 2022 ). In this study, high gene expression of TP6V1D had a positive impact on prediction, whereas low gene expression of ATP6V1D negatively impacted prediction, similar to CLIC1 .…”

Section: Discussionmentioning

confidence: 99%

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

et al. 2022

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Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method.Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis.Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.

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Section: Discussionmentioning

confidence: 99%

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

et al. 2022

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“…It was feasible for physicians to use our models to evaluate the risk of DM, because the predictors used by our models were readily available in clinical practice. Additionally, the SHAP method was used to better explain the results of XGB and RF models, which has been proved to be effective in several studies [29][30][31][32]. The outcomes of XGB and RF models could be clinically understandable and be visualized intuitively.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of machine learning based models for predicting distant metastasis in colorectal cancer: a population-level study

Wei

et al. 2022

Preprint

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Background Distant metastasis (DM) is an important prognostic factor and determines the following treatments in patients with colorectal cancer (CRC). The purpose of this study was to construct prediction models for DM in patients with CRC based on machine learning. Methods CRC patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were identified. Prediction models for DM were developed by applying four machine-learning methods including eXtreme Gradient Boost (XGB), decision tree (DT), random forest (RF), and support vector machine (SVM). The performance of models was quantitatively assessed by receiver operating characteristics (ROC) curve, calibration curve and decision curve analysis (DCA) curve. The SHapley Additive exPlanation (SHAP) method was used for visualization analysis to better explain the results of the machine learning models. Results A total of 51788 patients were identified in the SEER database. ROC curves exhibited excellent accuracy of machine learning models. Calibration curves for the probability of DM showed good agreement between model prediction and actual observation in both the training and validation cohorts. The DCA demonstrated that each machine learning model can provide net benefits with treat-none and treat-all strategies. In the SHAP summary plot of the RF and XGB models, carcinoembryonic antigen (CEA), N stage, T stage and tumor deposits were identified as the most important risk factors for DM. Conclusion The XGB and RF were ideal machine learning models and showed that CEA, N stage, T stage and tumor deposits were the most important DM-related risk factors.

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“…Given the inherent powerful feature of capturing the nonlinear relationships with machine learning algorithms, more researchers advocate the use of new prediction models based on machine learning to support appropriate treatment for patients rather than traditional illness severity classification systems such as SOFA, APACHE II, or SAPS II [9][10][11]. Although a large number of predictive models have shown promising performance in research, the evidence for their application in clinical setting and interpretable risk prediction models to aid disease prognosis are still limited [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Li¹,

Liu²,

Hu³

et al. 2022

J Med Internet Res

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Background Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF mortality prediction models have not yet reached clinical practice. Objective We aimed to develop an interpretable model to predict the mortality risk for patients with HF in intensive care units (ICUs) and used the SHapley Additive exPlanation (SHAP) method to explain the extreme gradient boosting (XGBoost) model and explore prognostic factors for HF. Methods In this retrospective cohort study, we achieved model development and performance comparison on the eICU Collaborative Research Database (eICU-CRD). We extracted data during the first 24 hours of each ICU admission, and the data set was randomly divided, with 70% used for model training and 30% used for model validation. The prediction performance of the XGBoost model was compared with three other machine learning models by the area under the curve. We used the SHAP method to explain the XGBoost model. Results A total of 2798 eligible patients with HF were included in the final cohort for this study. The observed in-hospital mortality of patients with HF was 9.97%. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.824 (95% CI 0.7766-0.8708), whereas support vector machine had the poorest generalization ability (AUC=0.701, 95% CI 0.6433-0.7582). The decision curve showed that the net benefit of the XGBoost model surpassed those of other machine learning models at 10%~28% threshold probabilities. The SHAP method reveals the top 20 predictors of HF according to the importance ranking, and the average of the blood urea nitrogen was recognized as the most important predictor variable. Conclusions The interpretable predictive model helps physicians more accurately predict the mortality risk in ICU patients with HF, and therefore, provides better treatment plans and optimal resource allocation for their patients. In addition, the interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians.

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Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP

Cited by 98 publications

References 31 publications

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

Development and validation of machine learning based models for predicting distant metastasis in colorectal cancer: a population-level study

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

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