🧪Yet Another ICU Benchmark: a holistic framework for the standardization of clinical prediction model experiments. Provide custom datasets, cohorts, prediction tasks, endpoints, preprocessing, and models. Paper: https://arxiv.org/abs/2306.05109

Repository for the Paper: „On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series“

Hypertension Risk Stratification Using Machine Learning: Lift-Based Evaluation on SAS® Viya With the Framingham Heart Study

Evaluates data efficiency in lung cancer risk prediction using a super-stacking ensemble. Trains models on progressively reduced fractions of the PLCO dataset while keeping a fixed test set, analyzing performance stability, degradation, and robustness under limited data.

End-to-end ML demo for clinical risk scoring on synthetic tabular patient data (data → features → models → metrics → FastAPI /predict).

Ethical approach to mental health screening using privacy-preserving machine learning for clinical decision support

Machine learning pipeline for multi-class treatment prediction in lung adenocarcinoma (LUAD) using patient-level molecular profiles, featuring ensemble-based model aggregation, benchmarking across diverse classifier architectures, and systematic performance evaluation.

A research-focused multimodal machine learning framework for clinical outcome prediction, integrating medical imaging and tabular clinical features with reproducible training and explainable AI components.

Early warning model for ICU cardiac arrest risk (eICU-CRD + BigQuery ML)

Clinical ML early sepsis risk: pipelines, conformal early-warning, capacity-aware triage, FastAPI serving.

CardioRiskAI is a machine learning–based clinical intelligence system for classifying myocardial infarction outcomes using structured patient data. It applies domain-aware preprocessing, class imbalance handling, and metric-driven model selection to support risk stratification and outcome awareness in healthcare settings.