About this blog: statistics, health and critical thinking
Reflections and tools to look beyond the numbers and the illusion of certainty
☕️About Me
Hello, I’m Maicel Eugenio Monzón Pérez. I’m a physician, biostatistician, and data scientist, passionate about transforming health research through science, statistics, and artificial intelligence. I graduated as a General Practitioner in 2003 and later pursued a Master’s in Health Informatics, the Biostatistics specialty (first and second degree), and finally a PhD in Medical Sciences at the University of Medical Sciences of Havana. Throughout my career, I have worked on the design and analysis of biomedical research, with a particular focus on clinical trials and the application of machine learning models to solve real-world problems in health. I also enjoy programming in R and Python, creating tools that enhance research efficiency and improve clinical decision-making. Today, I share my experience as an Assistant Professor at the National School of Public Health and as a Clinical Trial Reviewer at the Center for State Control of Medicines, Equipment, and Medical Devices. My purpose is clear: to strengthen a more robust, innovative, and evidence-based health system, bringing together medicine, statistics, and technology to generate real impact in people’s lives.
Interests
- Biostatistics
- Research Methodology
- Data Science
- Artificial Intelligence
- Clinical Trials
- Programming in R and Python
Education
General Practitioner (Basic Medical Degree)
University of Medical Sciences of Villa Clara
Master’s in Health Informatics
Center for Cybernetics Applied to Medicine (CECAM)
Specialist in Biostatistics
National School of Public Health
PhD in Medical Sciences
University of Medical Sciences of Havana
📚 My Research
My research spans a wide range of topics 📚, from the design and analysis of clinical trials and epidemiological studies 🔍, to the development and validation of predictive models for infectious diseases and triage systems 🧠. I aim to integrate biostatistics, epidemiology, and mathematical modeling to generate high-impact results 💭.
I am passionate about programming in R and Python, data analysis, machine learning, and Artificial Intelligence 🤖. My commitment is to advance biostatistics and research that transforms public health through innovative and collaborative approaches to improve people’s quality of life.
I am always open to collaborating on projects that push the boundaries of science and technology in healthcare.
Featured Publications
Recent Publications
(2025).
Music therapy and anxiety in Cuban adult patients undergoing tooth extractions.
Revista Habanera de Ciencias Médicas, 24.
(2023).
Tratamiento farmacológico y sus costos en fallecidos por COVID-19.
Revista Cubana de Farmacia, 56(2).
(2023).
Estado en la investigación sobre modelos de predicción de la severidad en confirmados de la Covid-19..
Revista Habanera, 1(1).
(2020).
Plagio en artículos de investigación en revistas biomédicas cubanas. 2016.
Revista Habanera de Ciencias Médicas, 19(4).
(2020).
Effects of policosanol in patients with metabolic syndrome: a six-month study.
Journal of Endocrinology and Metabolism, 10(2), 36-44.
Latest Blog Posts
A Classroom Experiment: The Day Silicon Handed Me the Nobel
Sequel to the ‘Lie to the Professor’ experiment. I challenged the silicon colossi with absurd correlations. Not one spotted the nonsense—revealing an algorithmic ‘horror vacui’ that poses a threat to scientific integrity.
Classroom Experiment: Can Students Fool a Biostatistician? A Data Fabrication Contest
I asked my residents to invent hemoglobin data in 60 seconds. I used six forensic techniques to catch them. Spoiler: Statistics always wins.
Writing Science or Rewriting Reality?
IMRaD is the publication standard, but it need not dictate the writing process. This post argues for a ‘reverse’ writing strategy—from findings back to the introduction—as a method to enhance honesty and clarity, distinguishing it from HARKing and aligning narrative with genuine scientific discovery.
An Intuitive Dive into LLM Architecture
This analysis examines the essential pillars that define Large Language Models (LLMs), from their technical conception to their practical implications and inherent challenges
📬 Shall We Work Together?
Interested in research collaboration, statistical consulting, or teaching? I am open to new projects.