Missing Data

TL;DR for operators CW-B is not interesting because it discovers a novel model architecture. It is interesting because it treats three routinely neglected design decisions as one system: Rare classes receive more influence during training. Class weights are computed separately inside each training fold, preventing common phenotypes from dominating the tree-building process. Missing values retain their provenance. The pipeline imputes a numerical value but also adds an indicator showing that the original measurement was absent. Clinical priorities are audited separately from aggregate performance. Stable coronary artery disease, acute coronary syndrome, and non-obstructive coronary disease are grouped into a predefined evaluation set because missing them can alter follow-up and treatment pathways. On a five-class dataset containing 4,354 patient records and 57 structured features, CW-B records the strongest accuracy, Macro-F1, balanced accuracy, and prioritized F1 among the tested tree, ensemble, and neural baselines. Its balanced accuracy reaches 0.73, compared with 0.66 for a larger XGBoost baseline. Its prioritized F1 is 0.69, compared with 0.67 for that baseline. ...

Scout reports are rarely complete. A basketball prospect may have clean scoring statistics, partial defensive records, uncertain positional labels, and only scattered evidence about career-stage potential. The team still has to make a decision. Waiting for perfect data is a charming fantasy, usually practiced by people who are not paying the salary bill. ...

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. ...