Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning

Figures

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  Fig. 1. Schematic overview of the study including predictive workflow and task definitions. (A) Illustration of the predictive workflow used in the study where model inputs include ECG waveforms, demographics, biometrics, and vital signs, which are used in a binary classification setting to predict abnormal laboratory values. (B) Demonstration of the abnormality prediction task, where we sample the closest vital signs within 30 minutes around the ECG record and use the closest laboratory value within 60 minutes as prediction target. (C) Demonstration of the abnormality forecasting task, where we also use closest vital signs within 30 minutes around the ECG record as input but where we predict the presence of any abnormal laboratory value within a predefined time horizon.

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  Fig. 2. Schematic overview of the dataset creation process, starting from three source datasets (MIMIC-IV-ECG, MIMIC-IV, and MIMIC-IV-ECG-ICD) and applying various inclusion criteria. The process includes patient selection equal or above 18 years old, unit standardization for laboratory values, outlier removal, and label definition based on median-derived thresholds. Similarly, we incorporate a special token in the label space for labels that were not available to sample for that specific sample, these tokens help as placeholders to be excluded during training and evaluation which ensure large availability of samples and labels even when some labels are not available for a sample. Additionally, we incorporated an imputation strategy to handle missing data with the use of binary masks as helpers e.g. one binary feature per each feature to let the model know which values are real and which imputed. Furthermore, we selected only labels with at least 20 available (10 positive and 10 negative) counts. Finally, we dropped samples that does not contain any available labels after the already mentioned steps.