Academic / healthcare ML · 2024-10-15
Sepsis Prediction Pipeline
Built and compared classification pipelines for sepsis prediction, including preprocessing, hyperparameter tuning, and model evaluation.
- Role
- ML project developer
- Contribution
- Built and compared classification pipelines for sepsis prediction, including preprocessing, hyperparameter tuning, and model evaluation.
- Evidence
- View public source ↗
Problem
This academic project explores whether clinical features can support sepsis prediction using supervised machine learning. It focuses on pipeline construction and model comparison.
Approach
I built preprocessing and classification workflows using Random Forest, XGBoost, and Logistic Regression, and explored hyperparameter tuning with Optuna. The work includes feature preparation and evaluation visualizations.
Evaluation
The public repository provides code and project evaluation artifacts. A meaningful assessment of a clinical prediction system also requires patient-level separation, time-aware evaluation, leakage checks, and external validation. The portfolio does not present the project's reported scores as proof of clinical effectiveness.
Outcome
The result is a reproducible project structure for comparing classifiers and examining their behavior. It is evidence of applied ML engineering and experimentation.
Limitations
This is academic work, not a clinically validated or deployed medical device. It does not establish earlier detection, patient benefit, or performance at a new hospital. Those claims require additional evidence beyond this project.