Freelance Medical Data Scientist

I specialize in statistical analysis and machine learning for medical, life-science, and public-health data. My work focuses on reproducible Python workflows, Bayesian modeling, complex survey analysis, predictive modeling, visualization, and technical reporting in English and Japanese.

Makoto Komori, Ph.D.
Freelance Medical Data Scientist

I provide support across the analytical workflow, from data preparation and study-oriented analysis design to modeling, validation, visualization, and technical documentation.

• Exploratory data analysis and descriptive statistics
• Group comparisons, regression analysis, and visualization
• Missing-data handling and MICE-style multiple imputation
• Statistical and medical interpretation of analytical results

• Linear and logistic regression
• Bayesian workflows using MCMC, including prior sensitivity analysis and posterior predictive checks
• Hierarchical Bayesian modeling
• Analysis accounting for complex survey designs
• Elastic Net and other regularized regression methods for variable selection
• Survey-weighted Gaussian GLM
• Univariable and multivariable quantile regression

• Classification and regression modeling with cross-validation
• Principal component analysis and clustering
• AutoML and model comparison with PyCaret
• Hyperparameter optimization with Optuna
• ROC, precision-recall, calibration, and decision curve analysis
• Model interpretation using SHAP

• Reusable functions and Python packages
• Jupyter notebooks and executable analysis scripts
• Unit testing with pytest
• Code-quality checks with Black and Flake8
• Continuous integration with GitHub Actions
• Technical reports in English and Japanese

A Bayesian logistic regression project for estimating dialysis-transition risk in patients with chronic kidney disease, with particular attention to rare-event modeling and uncertainty quantification.

Methods: prior sensitivity analysis · MCMC · stratified cross-validation · ROC-AUC · calibration · posterior predictive checks

A hierarchical Bayesian logistic regression project for estimating recurrence risk in patients with thyroid cancer.

Methods: PyMC · hierarchical modeling · MCMC diagnostics · LOO comparison · decision curve analysis

A machine-learning and multivariate-analysis project using blood-test data to build a liver-disease prediction model and explore latent biochemical patterns.

Methods: PyCaret · Extra Trees · Optuna · PCA · K-Means clustering · SHAP

An analysis of factors associated with liver fibrosis score using public NHANES data while accounting for complex survey design and missing data.

Methods: MICE-style multiple imputation · Elastic Net · survey-weighted Gaussian GLM · Rubin’s rules · quantile regression

• Ph.D. in Biotechnology
• Research background in biotechnology, in vitro diagnostic technologies, and clinical laboratory testing

• E Qualification, 2025 #1 — Passed
• Japan Deep Learning Association

“Measurement of microRNA with isothermal DNA amplification on fully automated immunoassay analyzers”
Analytical and Bioanalytical Chemistry (2019), 411, 3789–3800
https://doi.org/10.1007/s00216-019-01878-z

An educational and research prototype that combines skin images and questionnaire information to suggest candidate skin-disease categories.

Technical stack: DenseNet201 · AutoEncoder · BERT · Streamlit · FastAPI · Docker · Google Cloud Run

View Source Code

Responsible-use notice: This project is an educational and research prototype. It is not intended for clinical diagnosis, treatment decisions, decisions about seeking medical care, or use as a medical device.