AI-Enabled Clinical Decision Support System Modeling for the Prediction of Cirrhosis Complications - 29/08/24
, Meng-Yu Liu , Hsueh-Chun Lin ⁎ Abstract |
Background and Objective |
Utilizing artificial intelligence (AI), a clinical decision support system (CDSS), can help physicians anticipate possible complications of cirrhosis patients before prescribing more accurate treatments. This study aimed to establish a prototype of AI-CDSS modeling using electronic health records to predict five complications for cirrhosis patients who were controlled for oral antiviral drugs, lamivudine (LAM) or entecavir (ETV).
Methods |
Our modeling attained a web-based AI-CDSS with four steps – data extraction, sample normalization, AI-enabled machine learning (ML), and system integration. We designed the extract-transform-load (ETL) procedure to filter the analytics features from a clinical database. The data training process applied 10-fold cross-validation to verify diverse ML models due to possible feature patterns with medications for predicting the complications. In addition, we applied both statistical means and standard deviations of the realistic datasets to create the simulative datasets, which contained sufficient and balanced data to train the most efficient models for evaluation. The modeling combined multiple ML methods, such as support vector machine (SVM), random forest (RF), extreme gradient boosting, naive Bayes, and logistic regression, for training fourteen features to generate the AI-CDSS's prediction functionality.
Results |
The models achieving an accuracy of 0.8 after cross-validations would be qualified for the AI-CDSS. SVM and RF models using realistic data predicted jaundice with an accuracy of over 0.82. Furthermore, the SVM models using simulative data reached an accuracy of over 0.85 when predicting patients with jaundice. Our approaches implied that the simulative datasets based on the same distributions as that of the features in the realistic dataset were adequate for training the ML models. The RF model could reach an AUC of up to 0.82 for multiple complications by testing with the un-trained data. Finally, we successfully installed twenty models of the suitable ML methods in the AI-CDSS to predict five complications for cirrhosis patients prescribed with LAM or ETV.
Conclusions |
Our modeling integrated a self-developed AI-CDSS with the approved ML models to predict cirrhosis complications for aiding clinical decision making.
Le texte complet de cet article est disponible en PDF.Graphical abstract |
Highlights |
• | An AI-CDSS was established to predict five cirrhosis complications for patients. |
• | A 3-stage ETL procedure filters 14 features in 5 various tables for computation. |
• | Simulative datasets were derived from realistic datasets to train the best models. |
• | The modeling combined five machine learning methods to enable prediction functions. |
• | RF model can reach an area under ROC curve up to 0.82 for predicting complications. |
Keywords : Extract-transform-load process, Machine learning, Artificial intelligence, Clinical decision support system, Cirrhosis complications
Plan
Vol 45 - N° 5
Article 100854- octobre 2024 Retour au numéro
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