Objective: Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.
Materials and methods: We use a temporal convolutional network to encode longitudinal data and a feedforward neural network to encode demographic data from 4713 ICU admissions in 2014-2018. For each hour of each admission, we predict events in the subsequent 1-6 hours. We compare performance with other models including a recurrent neural network.
Results: Our model performed similarly to the recurrent neural network for some events and outperformed it for others. This performance increase was more evident in a sensitivity analysis where the prediction timeframe was varied. Average positive predictive value (95% CI) was 0.786 (0.781-0.790) and 0.738 (0.732-0.743) for up- and down-titrating FiO2, 0.574 (0.519-0.625) for extubation, 0.139 (0.117-0.162) for intubation, 0.533 (0.492-0.572) for starting noradrenaline, 0.441 (0.433-0.448) for fluid challenge, and 0.315 (0.282-0.352) for death.
Discussion: Events were better predicted where their important determinants were captured in structured electronic health data, and where they occurred in homogeneous circumstances. We produce partial dependence plots that show our model learns clinically-plausible associations between its inputs and predictions.
Conclusion: Temporal convolutional networks improve prediction of clinical events when used to represent longitudinal ICU data.
Keywords: airway extubation; artificial intelligence; critical care; machine learning; oxygen inhalation therapy.
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