Key result
Random Forest model predicts tachycardia in pulmonary thromboembolism patients with ~0.93 AUROC.
Why the study?
Most machine-learning studies in cardiovascular disease emphasize detection of tachycardia rather than prospective prediction using routinely collected physiological data.
Can time-aware machine learning models prospectively predict tachycardia in hospitalized patients with cardiovascular disease?
Can time-aware machine learning models prospectively predict tachycardia in hospitalized patients with cardiovascular disease?
Effect estimate: AUROC 0.93 ± 0.02
Time-aware machine learning models, particularly Random Forest, demonstrate strong internal feasibility for prospectively predicting tachycardia in hospitalized cardiovascular patients using routine monitoring data.
Supports internal feasibility of ML tachycardia prediction in PE; leaves open external validation before clinical adoption.
Tachycardia can accompany acute deterioration in hospitalized patients with cardiovascular disease, yet most machine-learning studies emphasize detection rather than prospective prediction. Using routinely collected physiological monitoring data from a hospital-wide database of 512,764 patients, we identified 968 patients with myocardial infarction (MI; n = 260), pulmonary thromboembolism (PTE; n = 48), or aortic stenosis (AS; n = 660) and developed models for prospective tachycardia prediction. Tachycardia was defined as heart rate >100 bpm. Historical physiological features were separated temporally from the future target, population-level autocorrelation analysis informed lag selection, and Random Forest (RF), Naïve Bayes (NB), and a feedforward neural network (NN) were evaluated using patient-blocked, time-aware validation. At the selected 90-step lag configuration, RF showed the strongest performance, including an F1-score of 0.89 ± 0.02, PR-AUC of 0.72 ± 0.03, and AUROC of 0.93 ± 0.02 for PTE. These findings provide internal feasibility evidence for time-aware tachycardia risk prediction from routine hospital monitoring data; external and prospective validation is required before clinical deployment.
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Ferreira et al. (2026) studied Cardiovascular disease (myocardial infarction, pulmonary thromboembolism, or aortic stenosis) (n=968). Time-aware machine learning models (Random Forest) vs. Naïve Bayes and feedforward neural network was evaluated on Prospective tachycardia prediction (heart rate >100 bpm) (AUROC 0.93 ± 0.02). A Random Forest machine learning model prospectively predicted tachycardia in patients with pulmonary thromboembolism with an AUROC of 0.93 ± 0.02.
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