May 11, 2026
Can an ICU prediction model estimate several possible patient outcomes without contradicting itself over time? This project develops Joint-CE (Joint Competing-Event), a machine-learning model that treats major ICU outcomes as competing possibilities—meaning the first event to occur determines the patient’s next clinical state. Using MIMIC-IV and eICU-CRD, Joint-CE predicts both what may happen next and when across multiple future horizons. Unlike separate prediction models, it produces one coherent probability distribution across outcomes and time, preventing logically contradictory risk estimates.
The model focuses on patients who have not yet received major organ support and repeatedly updates predictions during their ICU stay. At each landmark, it estimates the first major event that may occur: invasive ventilation, vasopressor support, renal replacement therapy (RRT), combined ventilation and vasopressor support, death, or live discharge. If none occurs within the prediction window, the patient is considered event-free. Predictions are generated for 24-, 48-, 72-, and 120-hour horizons.
The main contribution of the project is its approach to probability coherence. Conventional methods can train a separate model for each outcome and time horizon, which may produce contradictory predictions—for example, estimating a lower cumulative risk of ventilation at 48 hours than at 24 hours. Joint-CE instead models event type and timing together in a single probability distribution, so cumulative event risks cannot decrease over time and event-free probability cannot increase.
Joint-CE was evaluated using two large critical-care databases, MIMIC-IV and eICU-CRD, and compared with a conventional approach using 28 independently trained boosted-tree classifiers. The model maintained comparable or modestly better overall predictive performance while eliminating cross-horizon probability contradictions. It was also transported directly from MIMIC-IV to 55 held-out eICU hospitals without fitting or recalibration on the target database, providing a demanding test of how well the approach transfers across different clinical settings.
Rather than showing that one model can simply achieve a higher accuracy score, this project demonstrates that dynamic ICU predictions can remain internally consistent across both outcomes and time without an evident predictive penalty.
Paper is coming soon….
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MedMuse