An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients

Peng, Junfeng; Zou, Kaiqiang; Zhou, Mei; Teng, Yinglei; Zhu, Xiongyong; Zhang, Feifei; Xu, Jun

doi:10.1007/s10916-021-01736-5

Cited by 61 publications

(24 citation statements)

References 18 publications

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“…Through our understandable prediction and feature selection phase, it is possible to determine which features of datasets were more important in prediction. This explainable COVID-19 disease diagnosis model has higher transparency and explainability than previous black box methods [ [67] , [68] , [69] , [70] , [103] , [104] , [105] ] that can improve the acceptance rate and trustworthy of intelligent model for physicians.…”

Section: Discussionmentioning

confidence: 97%

“…In overall, we noticed that in the diagnosis of COVID-19, complex machine-learning models such as deep learning perform better than simple models such as linear regression and decision trees. Nevertheless, the deep learning-based approaches proposed in previous works were indeed black boxes that did not explain their prediction in a manner a human could understand [ [67] , [68] , [69] , [70] ]. It is therefore important to endow the highly performing deep-learning models with explainability and interpretability ability to accommodate the new EU data protection directive and ensure their widespread adoption by healthcare authorities.…”

Section: Introductionmentioning

confidence: 99%

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A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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“…Mehbodniya et al ( 8 ) used machine learning to classify fetal health from cardiotocographic data. Peng et al ( 9 ) used an explainable artificial intelligence framework to predict deterioration risk of hepatitis patients. Hu et al ( 10 ) used deep learning system to identify lymph node quantification and metastatic cancer.…”

Section: Introductionmentioning

confidence: 99%

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Zhang

Zhong

et al. 2022

Front. Public Health

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BackgroundPancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM.MethodsPC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves.ResultsA total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power.ConclusionWe developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

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“…Nonetheless, the findings of this study will be useful to improve existing predictive models in future research; in particular, multi-center data should be included and external tests should be conducted rigorously regarding the predictions. Finally, to fully determine the black-box nature of the ML model, this study followed several previous studies (22,41,42) by using the SHAP method for global interpretation and LIME for local interpretation. Although the results of both types of interpretation of the XGBoost model were consistent and credible, improved robustness could be attained by using other interpretation methods, such as Shapley Lorenz, which is a novel global interpretation method that provides a global normalized measure of explainability.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients

Guo

Fang

Yang

et al. 2021

Front. Cardiovasc. Med.

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Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection.Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model.Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860–0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

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An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients

Cited by 61 publications

References 18 publications

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients

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