Predicting Post-Surgical Complications with Quantum Neural Networks: A Clinical Study on Anastomotic Leak

• URL: http://arxiv.org/abs/2506.01708v1
• Date: Mon, 02 Jun 2025 14:13:10 GMT
• Title: Predicting Post-Surgical Complications with Quantum Neural Networks: A Clinical Study on Anastomotic Leak
• Authors: Vojtěch Novák, Ivan Zelinka, Lenka Přibylová, Lubomír Martínek, Vladimír Benčurik,
• Abstract summary: We explore the application of quantum machine learning in medical diagnostics.<n>This study focuses on the prediction of anastomotic leak, a severe post-surgical complication.
• Score: 0.16777183511743468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing represents a transformative leap in computational power, with the potential to revolutionize various fields, including healthcare. In this study, we explore the application of quantum machine learning in medical diagnostics, specifically focusing on the prediction of anastomotic leak, a severe post-surgical complication. By comparing quantum neural networks implemented with different ansatz functions to classical classification approaches-including logistic regression, support vector machines, multilayer perceptron, naive Bayes, nearest neighbors, and ensemble methods like gradient boosting and AdaBoost-we evaluate their effectiveness in predictive modeling. All models were trained and evaluated using simulations on a classical computer, without quantum sampling, noise, or other quantum-related errors. We employed cross-validation to ensure that model performance was assessed on unseen data, minimizing overfitting. Given the clinical importance of identifying true positives, we analyzed predictions at the highest possible sensitivity levels. At these sensitivity levels, quantum neural networks using the RealAmplitudes ansatz demonstrated the best performance in terms of positive predictive value, negative predictive value, specificity, Brier score, log loss, and other classification metrics. Our findings highlight the potential of quantum machine learning to improve predictive accuracy in complex medical scenarios and underscore the promise of integrating quantum computing into healthcare for better diagnostic outcomes.

Related papers

• Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies [0.17999333451993949]
  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)<n>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.<n>These results highlight QNNs' potential, enhanced by CMA-ES and noise-aware strategies, to improve causal inference in biomedical research
  arXiv  Detail & Related papers  (2025-06-24T19:40:39Z)
• Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy [0.9999629695552196]
  The present work develops and validates a data-driven and interpretable machine-learning framework designed to predict strokes.<n>Ten routinely gathered demographic, lifestyle, and clinical variables were sourced from a public cohort of 4,981 records.<n>The proposed model achieved an accuracy rate of 97.2% and an F1-score of 97.15%, indicating a significant enhancement compared to the leading individual model.
  arXiv  Detail & Related papers  (2025-05-18T21:46:45Z)
• Comparative Analysis of Quantum Support Vector Machines and Variational Quantum Classifiers for B-cell Epitope Prediction in Vaccine Design [0.0]
  This study investigates two quantum machine learning models-the Quantum Support Vector Machine (QSVM) and the Variational Quantum (VQC)-in the context of B-cell prediction.
  arXiv  Detail & Related papers  (2025-04-14T10:19:33Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks [0.0]
  This study presents a Hybrid Quantum Convolutional Neural Network (HQCNN) that integrates quantum feature-encoding circuits with depth-wise separable convolutional layers to analyze images from a publicly available brain tumor dataset.<n>The HQCNN achieved 99.16% training accuracy and 91.47% validation accuracy, demonstrating robust performance across varied imaging conditions.
  arXiv  Detail & Related papers  (2024-01-28T23:27:06Z)
• An Evaluation of Machine Learning Approaches for Early Diagnosis of Autism Spectrum Disorder [0.0]
  Autistic Spectrum Disorder (ASD) is a neurological disease characterized by difficulties with social interaction, communication, and repetitive activities. This study employs diverse machine learning methods to identify crucial ASD traits, aiming to enhance and automate the diagnostic process.
  arXiv  Detail & Related papers  (2023-09-20T21:23:37Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Improved clinical data imputation via classical and quantum determinantal point processes [1.3749490831384268]
  Imputing data is a critical issue for machine learning practitioners. Here we propose novel imputation methods based on determinantal point processes. We demonstrate competitive results with up to ten qubits for small-scale imputation tasks.
  arXiv  Detail & Related papers  (2023-03-31T08:54:46Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• Learning brain MRI quality control: a multi-factorial generalization problem [0.0]
  This work aimed at evaluating the performances of the MRIQC pipeline on various large-scale datasets. We focused our analysis on the MRIQC preprocessing steps and tested the pipeline with and without them. We concluded that a model trained with data from a heterogeneous population, such as the CATI dataset, provides the best scores on unseen data.
  arXiv  Detail & Related papers  (2022-05-31T15:46:44Z)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology [18.069876260017605]
  Quantum Machine Intelligence offers unparalleled insights into tumor dynamics via a quantum mechanics perspective. We introduce a novel hybrid quantum-classical neural architecture named $eta-$Net that enables quantifying quantum dynamics of tumor burden concerning treatment effects.
  arXiv  Detail & Related papers  (2022-02-21T08:46:58Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Variational learning for quantum artificial neural networks [0.0]
  We first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. While keeping full compatibility with the overall memory-efficient feed-forward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons.
  arXiv  Detail & Related papers  (2021-03-03T16:10:15Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome [62.997667081978825]
  We implement a two-step approach to multi-omic logistic regression in which variable selection is performed on each layer separately. Our approach should be preferred if the goal is to select as many relevant predictors as possible. Our proposed approach allows us to identify features that characterise cardiometabolic syndrome at the molecular level.
  arXiv  Detail & Related papers  (2020-08-01T10:36:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.