arXiv subjects

Lenka Přibylová

Publications and source records attributed to Lenka Přibylová.

3 recordsLinked to original sources

arXiv2026

Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors

This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14\% leak prevalence, we tested ZZFeatureMap encodings with RealAmplitudes and EfficientSU2 ansatze under simulated noise. $F_β$-optimized quantum configurations yielded significantly higher sensitivity (83.3\%) than classical baselines (66.7\%). This demonstrates that quantum feature spaces better prioritize minority class identification, which is critical for low-prevalence clinical risk prediction. Our work explores various optimizers under noisy conditions, highlighting key trade-offs and future directions for hardware deployment.

cs.LG↗

arXiv2025

Quantum Machine Learning for Predicting Anastomotic Leak: A Clinical Study

Anastomotic leak (AL) is a life-threatening complication following colorectal surgery, and its accurate prediction remains a significant clinical challenge. This study explores the potential of Quantum Neural Networks (QNNs) for AL prediction, presenting a rigorous benchmark against hyperparameter-tuned classical models including logistic regression, multilayer perceptrons, and boosting algorithms. Using a clinical dataset of 200 patients and four key predictors identified through statistical analysis, we evaluated QNNs with ZZFeatureMap encoding and EfficientSU2 and RealAmplitudes ansätze simulated under realistic hardware noise models. Our framework emphasizes robustness, with performance metrics averaged over 10 independent optimization runs using multiple algorithms. The EfficientSU2-BFGS combination achieved the highest mean AUC of $0.7966 \pm 0.0237$, while RealAmplitudes with CMA-ES excelled in Average Precision ($0.5041 \pm 0.1214$), critical for imbalanced medical datasets. We establish a direct link between optimizer convergence and model efficacy, where effective variational parameter optimization translates to improved classification metrics. Interpretability analysis suggests QNNs may capture complex, non-linear feature relationships not evident in classical linear models. This work highlights QNNs' potential as screening tools while underscoring the need for model selection based on specific clinical goals, pending validation on larger datasets.

quant-ph↗

arXiv2025

Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies

This study investigates the application of quantum neural networks (QNNs) for propensity score estimation to address selection bias in comparing survival outcomes between laparoscopic and open surgical techniques in a cohort of 1177 colorectal carcinoma patients treated at University Hospital Ostrava (2001-2009). Using a dataset with 77 variables, including patient demographics and tumor characteristics, we developed QNN-based propensity score models focusing on four key covariates (Age, Sex, Stage, BMI). The QNN architecture employed a linear ZFeatureMap for data encoding, a SummedPaulis operator for predictions, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for robust, gradient-free optimization in noisy quantum environments. Variance regularization was integrated to mitigate quantum measurement noise, with simulations conducted under exact, sampling (1024 shots), and noisy hardware (FakeManhattanV2) conditions. QNNs, particularly with simulated hardware noise, outperformed classical logistic regression and gradient boosted machines in small samples (AUC up to 0.750 for n=100), with noise modeling enhancing predictive stability. Propensity score matching and weighting, optimized via genetic matching and matching weights, achieved covariate balance with standardized mean differences of 0.0849 and 0.0869, respectively. Survival analyses using Kaplan-Meier estimation, Cox proportional hazards, and Aalen additive regression revealed no significant survival differences post-adjustment (p-values 0.287-0.851), indicating confounding bias in unadjusted outcomes. These results highlight QNNs' potential, enhanced by CMA-ES and noise-aware strategies, to improve causal inference in biomedical research, particularly for small-sample, high-dimensional datasets.

quant-ph↗