biomed-clinical-data-ml-and-bioinformaticslisted

# Biomedical Engineering: Clinical Data, Clinical ML, and Bioinformatics > **Part 2 of 5** of the *Biomedical Engineering* reference (plugin `biomedical-engineering-technical`), covering §3–§5. Sibling skills: `biomed-signals-and-medical-imaging` (§0–§2), `biomed-structural-systems-biology-and-pharmacology` (§6–§9), `biomed-biomechanics-devices-and-biostatistics` (§10–§15), `biomed-reference` (§16–§20). Section numbers are shared across the set; a reference written as §N → `skill` points into that sibling skill. > > **Currency:** The physics, physiology and mathematics are stable; tool and pipeline recommendations shift slowly. > **Scope note.** This is the engineering and science. **Regulatory pathways, quality > systems, and lifecycle process are deliberately excluded** — they're a separate subject > and they'd swamp the technical content. > > **⚠️ GOTCHA** boxes mark where a silent wrong answer is produced — which in this domain > is the dangerous failure mode, not a crash. > > **The three technical facts that recur everywhere below:** > 1. **⚠️ Biological signals are non-stationary, and most DSP assumes stationarity.** Every > windowing choice is an assumption about how long the physiology holds still (§1 → `biomed-signals-and-medical-imaging`). > 2. **⚠️ Prevalence governs predictive value.** Sensitivity and specificity are properties > of a test; PPV is a property of a test *in a population*. Confusing them is the single > most common quantitative error in th