An explainable algorithm for detecting drug-induced QT-prolongation at risk of torsades de pointes (TdP) regardless of heart rate and T-wave morphology

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

“…The 340 articles that did not mention XAI models or clinical use in the title or abstract were excluded during the title/abstract screening process. In five studies, the aim was to develop an explanatory model to assist healthcare providers in diagnosing a patient's disease [ [29] , [30] , [31] , [32] , [33] ]. The aim of the remaining study was to examine the explainability of the model in order to identify variables that have an important influence on the prediction results [ 34 ].…”

“…The aim of the remaining study was to examine the explainability of the model in order to identify variables that have an important influence on the prediction results [ 34 ]. Four of the six studies utilized publicly available clinical data [ [29] , [30] , [31] , [32] ], and the other two studies utilized electronic health record (EHR) data [ 33 , 34 ]. The studies mainly used image and multimedia data, such as computed tomography (CT) scans [ 33 ], pathogen images [ 32 ], electrocardiogram (ECG) [ 29 , 31 ], ultrasound videos [ 30 ], and operation videos [ 34 ].…”

“…Various types of AI algorithms, including the rule-based algorithm [ 29 ], K-Nearest Neighbors (KNN) [ 31 ], Convolutional Neural Network (CNN) [ [30] , [31] , [32] ], conventional image processing algorithm [ 33 ], and Support Vector Machine (SVM) [ 34 ] were used in the studies. The performance of the algorithms was presented using accuracy, balanced accuracy, sensitivity or recall, specificity, Positive Predictive Value (PPV) or precision, Negative Predictive Value (NPV), F1-score, Area Under the Receiver Operating Characteristic Curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), Matthew's Correlation Coefficient (MCC), and error rate.…”

Added value of patient‐ and drug‐related factors to stratify drug‐drug interaction alerts for risk of QT prolongation: Development and validation of a risk prediction model

“…Another problem is that automated QT measurements might be inaccurate, especially in the case of morphologic abnormalities in the T wave 7,53,55,56 . Automated detection of QTc prolongation using (explainable) artificial intelligence or other novel techniques is very promising to address this important clinical need 57–59 . On the other hand, manual QT measurements are time‐consuming and can be often inaccurate 45,60 .…”

“…7,53,55,56 Automated detection of QTc prolongation using (explainable) artificial intelligence or other novel techniques is very promising to address this important clinical need. [57][58][59] On the other hand, manual QT measurements are time-consuming and can be often inaccurate. 45,60 In the current study, 31% of patients had…”