Drug Delivery & Healthcare Data

Pharmacokinetics, statistics and medical AI. First-order ODEs and pharmacokinetic models for dosing and controlled release, probability and Bayes for diagnostics, and machine learning evaluated the way clinicians need.

• First-Order ODEs — The workhorse equations of engineering. Learn separable equations, homogeneous (y/x) equations, exact equations and integrating factors, linear first-order equations with the integrating factor e^∫P, Bernoulli equations, orthogonal trajectories, and applications: RC/RL circuits, Newton's cooling, mixing tanks, population growth and motion with drag — with an RK4 check.
• Pharmacokinetics & Drug Delivery Systems — Modelling how drugs move through the body. Learn ADME (absorption, distribution, metabolism, excretion), one- and two-compartment models, volume of distribution, clearance and half-life, IV bolus, infusion and oral dosing, repeated dosing and steady state, therapeutic windows, controlled-release systems (diffusion, erosion, osmotic pumps), nanoparticles and targeted delivery — with Python simulating dosing regimens.
• Probability Fundamentals & Bayes — Probability quantifies uncertainty. Learn experiments, sample spaces and events, the axioms, complements, unions and intersections, conditional probability, independence, the multiplication rule, total probability, Bayes' theorem, and reliability of series and parallel systems.
• Healthcare Data Science & Medical AI — Turning medical data into decisions. Learn medical data types (signals, images, EHR, genomics), diagnostic test metrics (sensitivity, specificity, PPV, NPV, ROC/AUC) and the effect of prevalence, machine-learning basics (logistic regression, trees, CNNs), training/validation/test splits and leakage, bias and fairness, explainability, clinical validation and regulation of AI as a medical device — with Python computing test metrics and training a logistic-regression model.