Filling the Gaps: How Bayesian Networks Learn to Guess Smarter in Intensive Care
ICU data has a habit of disappearing exactly when analysts would prefer it to behave. A blood gas is not measured. A pressure reading arrives late. A neurological score is absent because the patient is sedated, unstable, transferred, or simply surrounded by humans doing triage instead of satisfying a data scientist’s spreadsheet fantasies. Then, after the ward has produced this imperfect record, a model is asked to infer how the patient’s physiology evolved over time. ...