Data Science

Data science applications in healthcare and medical research

When “Not Worse” Is Enough: Understanding Non-Inferiority Trials in Medicine

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Non-inferiority Trials in Medicine: Last updated: June 2026 Author: Michele D. Pierri Reading time: 15–20 minutes In clinical research, we are used to thinking that a new drug, device, or procedure should be tested by asking a simple question: Is it better than what we already have? This is the logic of a superiority trial. … Read more

Natural Language Processing for Medical Report Analysis – Part 3

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A doctor reads a discharge letter to a patient.

Machine Learning and Large Language Models for Clinical Text Analysis Article authored by Michele D. Pierri, MD Cardiac Surgeon & Medical Technology Researcher Last updated: May 2025 Reading time: 30 minutes Abstract The first two articles in this series traced a progression from rule-based pattern matching (regular expressions) to linguistic analysis (spaCy/scispaCy) for extracting structured … Read more

Non-Parametric Statistics in Medicine

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Non-Parametric Statistics in Medicine: A Practical Guide for Skewed, Ordinal, and Small Clinical Datasets Last updated: May 2026 Author: Michele D. Pierri Reading time: 15–20 minutes Introduction Clinical data rarely behave like the textbook examples we encounter in introductory statistics courses. Length of stay tends to be right-skewed. Biomarkers such as C-reactive protein, ferritin, D-dimer, … Read more

Natural Language Processing for Medical Report Analysis – Part 2

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A doctor translates text

Introduction to Natural Language Processing for Medical Report Analysis: Advanced Linguistic Analysis with spaCy and scispaCy for Medical Reports Article authored by Michele D. Pierri, MD Cardiac Surgeon & Medical Technology Researcher Last updated: May 2025 Reading time: 30 minutes Abstract Regular expressions give us a solid foundation for pulling structured data out of clinical … Read more

Natural Language Processing for Medical Report Analysis – Part 1

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Painterly rural scene at sunset, where a solitary man studies an immense handwritten scroll by candlelight beside a thatched farmhouse, surrounded by hay bales, a wooden desk, an antique typewriter, barrels, and farming tools.

Introduction to Natural Language Processing for Medical Report Analysis: Pattern Matching with Regular Expressions Article authored by Michele D. Pierri, MD Cardiac Surgeon & Medical Technology Researcher Last updated: January 2025 Reading time: 25 minutes Abstract Medical discharge summaries contain critical clinical information that remains largely inaccessible to automated analysis due to their unstructured nature. … Read more

Turbo Regression

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How to Put a Turbo on Regression Models Bootstrap Validation and Cubic Splines for Medical Data A practical guide for data scientists and clinical researchers working with limited medical datasets Last updated: December 2025 Author: Michele D. Pierri Reading time: 15–20 minutes Introduction — Why regression often disappoints in medicine Regression models are still the … Read more

Multiple Imputation for Missing Data

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A young boy kneels beside the shattered remains of a clay pot in a sunlit, rustic room, surrounded by warm golden light, long shadows, and aged earthenware in a melancholic early-20th-century painterly style.

A Complete Guide to Multiple Imputation for Missing Data: When, How, and Why Last updated: November 2025 Author: Michele D. Pierri Reading time: 15–20 minutes Glossary MCAR: Missing Completely At Random. Missingness is unrelated to observed or unobserved data; complete case can be unbiased. MAR: Missing At Random. Missingness depends only on observed variables; standard … Read more

Decision Curve Analysis

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A physician in a pale coat stands at a forked mountain path at sunset, holding a glowing compass between two wooden signs marked “TREAT NONE” and “TREAT ALL,” symbolizing a clinical decision between opposing treatment strategies.

Introduction Decision Curve Analysis (DCA) is a powerful tool for evaluating the clinical utility of predictive models and diagnostic tests. Unlike traditional metrics like AUC or calibration, DCA focuses on what truly matters in practice: whether using a model leads to better decisions and outcomes. DCA was introduced by Vickers and Elkin in 2006 to … Read more

Effect Size

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A glowing magnifying glass highlights “p < 0.05” beside a winding path toward a mountain marked “Large Effect Size,” while a roadside sign warns that statistical significance is not the same as practical importance.

Effect Size: What It Is and Why It Matters More Than Statistical Significance A result can be statistically significant — yet practically meaningless. Learn how effect size reveals the real-world impact of research findings. Introduction: The Hidden Problem with p-values You’ve probably seen headlines like: “New Study Shows Coffee Improves Memory!” But what if the … Read more

Calibration of Predictive Risk Models: A Guide for Clinicians

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An elderly physician in a white coat carefully shapes a red calibration curve on a large graph comparing predicted and observed risk, in a warmly lit early 20th-century medical study rendered in muted sepia and ochre tones.

Introduction: Understanding Calibration Challenges Consider a thermometer that perfectly identifies when one temperature is higher or lower than another, but consistently reads 5 degrees too high. This thermometer has good discrimination (it correctly ranks temperatures), but poor calibration (its absolute values are inaccurate). The same problem occurs with clinical risk models such as EuroSCORE: they … Read more