Schema-First Pipelines for Clinical Signals: Tidy Structuring as the Engine of Health Data Analytics

This paper shows how you can reframe the analytical step of preprocessing as a first-class analytic step in health datasets. Furthermore, it sets out a tidy schema, which is each variable is a column, each observation is a row, each observational unit is a table etc. A suite of compact tools are prescribed which includes melting, splitting, casting, normalization etc. These tools can transform heterogeneous clinical, epidemiologic, and sensor records into interoperable, model-ready tables. Through a worked mortality case that engineers hourly cause-of-death profiles, and joins codebooks, constructs proportions, it demonstrates how schema-first tidying boosts downstream visualization, modeling, and reproducible workflows. In addition, it provides concrete implementation guidance through composable tools. Finally, it illustrates how standardizing structures improves validity, auditability, and scalability across health data analytics pipelines.