Daria Kurz

Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning

This paper introduces a machine learning system that discovers mathematical and physical relations in oncology data, revealing human-understandable insights into cancer dynamics and treatment responses.

PERFECTO: Prediction of Extended Response and Growth Functions for Estimating Chemotherapy Outcomes in Breast Cancer

PERFECTO predicts tumor growth and chemotherapy response using clinical data to improve personalized breast cancer treatment planning.

Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

GLUECK, a neural network model, learns cancer growth curves from clinical datasets without prior knowledge, outperforming traditional tumor growth models.

Prediction of Individual Breast Cancer Evolution to Surgical Size

The paper introduces a Machine Learning model called PRINCESS to predict individual breast cancer evolution to surgical size, showing superior accuracy compared to existing models.

Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

CHIMERA combines mechanistic models and machine learning to optimize chemotherapy and surgery sequencing in breast cancer by learning tumor growth and drug pharmacokinetics.

Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast Cancer Data

This paper introduces a machine learning model that learns tumor growth patterns and volume prediction from breast cancer data without supervision, outperforming existing models on clinical datasets.