An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients.
In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. Howeve...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 5; pp. 1 - 10 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150234016&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150234016 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2021 vid: 45 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150234016 150234016 150234016 10.1007/s10916-021-01736-5 150234016 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients. aug: au: Peng, Junfeng Zou, Kaiqiang Zhou, Mi Teng, Yi Zhu, Xiongyong Zhang, Feifei Xu, Jun affil: Department of Computer Science, Guangdong University of Education, 510303, Guangzhou, China sug: subj: Hepatitis Diagnosis Diagnosis, Computer Assisted Clinical Deterioration Risk Factors Risk Assessment Artificial Intelligence Human Descriptive Statistics Funding Source Decision Making, Clinical Deep Learning Hepatitis Physiopathology Logistic Regression Decision Trees Support Vector Machine Random Forest Models, Statistical ab: In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. However,complex model as a black box, which ignores why it make a certain decision, causes the model distrust from clinicians. To solve these issues, an explainable artificial intelligence (XAI) framework is proposed in this paper to give the global and local interpretation of auxiliary diagnosis of hepatitis while retaining the good prediction performance. First, a public hepatitis classification benchmark from UCI is used to test the feasibility of the framework. Then, the transparent and black-box machine learning models are both employed to forecast the hepatitis deterioration. The transparent models such as logistic regression (LR), decision tree (DT)and k-nearest neighbor (KNN) are picked. While the black-box model such as the eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forests (RF) are selected. Finally, the SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME) and Partial Dependence Plots (PDP) are utilized to improve the model interpretation of liver disease. The experimental results show that the complex models outperform the simple ones. The developed RF achieves the highest accuracy (91.9%) among all the models. The proposed framework combining the global and local interpretable methods improves the transparency of complex models, and gets insight into the judgments from the complex models, thereby guiding the treatment strategy and improving the prognosis of hepatitis patients. In addition, the proposed framework could also assist the clinical data scientists to design a more appropriate structure of CAD. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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