Figure 4. Machine learning workflow in this study.

(1) Data preprocessing includes noise filtering and normalization of the drug-free and drugged induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) and adult cardiomyocyte (adult-CM) action potentials (APs). (2) Incorporating the preprocessed iPSC-CM APs as input and adult-CM APs and corresponding labels (drug-free [0] and drugged [1]) of iPSC-CM APs as targets into the multitask network. (3) Splitting the input and target data into training, validation and test set, and using training and validation set for training and tuning the network hyperparameters. (4) Comparing the network performance for training set and validation set to decide when to stop training and tuning the network hyperparameters. (5) Testing the overall multitask network performance using holdout test dataset and removing the long-short-term-memory (LSTM) layers, classification task (model ablation), and iPSC-CM AP values at different time frames (feature ablation) to study the performance of the network in the absence of its building blocks.