Cardiovascular Pharmacology in Pediatric Patients with Congenital Heart Disease

Dabbagh, Ali; Talebi, Zahra; Rajaei, Samira

doi:10.1007/978-3-319-44691-2_4

Cited by 3 publications

(1 citation statement)

References 180 publications

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“…These waveform records include thousands of recordings of waveforms (such as one or more channel of ECG signals) and the time series of vital signs (such as heart and respiration rates). This research chose 18 frequently used rescue intravenous drugs in critical care unit (CCU) [64], which is a special department of the ICU, and got 19,608 experimental records. These medications include sodium nitroprusside, nitroglycerin, dopamine, dobutamine, norepinephrine, milrinone, amiodarone, lidocaine, epinephrine, adenosine, alteplase, esmolol, diltiazem, phenylephrine, hydralazine, nesiritide, procainamide, and isoproterenol.…”

Section: Datasetmentioning

confidence: 99%

Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model

Xie

Wang

et al. 2022

Sensors

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Modern healthcare practice, especially in intensive care units, produces a vast amount of multivariate time series of health-related data, e.g., multi-lead electrocardiogram (ECG), pulse waveform, blood pressure waveform and so on. As a result, timely and accurate prediction of medical intervention (e.g., intravenous injection) becomes possible, by exploring such semantic-rich time series. Existing works mainly focused on onset prediction at the granularity of hours that was not suitable for medication intervention in emergency medicine. This research proposes a Multi-Variable Hybrid Attentive Model (MVHA) to predict the impending need of medical intervention, by jointly mining multiple time series. Specifically, a two-level attention mechanism is designed to capture the pattern of fluctuations and trends of different time series. This work applied MVHA to the prediction of the impending intravenous injection need of critical patients at the intensive care units. Experiments on the MIMIC Waveform Database demonstrated that the proposed model achieves a prediction accuracy of 0.8475 and an ROC-AUC of 0.8318, which significantly outperforms baseline models.

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Section: Datasetmentioning

confidence: 99%