Examinando por Autor "Sierra-Sosa, Daniel"

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    Quantum machine learning applications in the biomedical domain: a systematic review
    (Institute of Electrical and Electronics Engineers Inc., 2022) Maheshwari, Danyal; García-Zapirain, Begoña; Sierra-Sosa, Daniel
    Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning has become quite a challenging field in the case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models and quantum circuits using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.
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    Scalable healthcare assessment for diabetic patients using deep learning on multiple GPUS
    (IEEE Computer Society, 2019-10) Sierra-Sosa, Daniel; García-Zapirain, Begoña; Castillo Olea, Cristian; Oleagordia Ruiz, Ibon; Nuño Solinís, Roberto; Urtaran Laresgoiti, Maider; Elmaghraby, Adel Said
    The large-scale parallel computation that became available on the new generation of graphics processing units (GPUs) and on cloud-based services can be exploited for use in healthcare data analysis. Furthermore, computation workstations suited for deep learning are usually equipped with multiple GPUs allowing for workload distribution among multiple GPUs for larger datasets while exploiting parallelism in each GPU. In this paper, we utilize distributed and parallel computation techniques to efficiently analyze healthcare data using deep learning techniques. We demonstrate the scalability and computational benefits of this approach with a case study of longitudinal assessment of approximately 150 000 type 2 diabetic patients. Type 2 diabetes mellitus (T2DM) is the fourth case of mortality worldwide with rising prevalence. T2DM leads to adverse events such as acute myocardial infarction, major amputations, and avoidable hospitalizations. This paper aims to establish a relation between laboratory and medical assessment variables with the occurrence of the aforementioned adverse events and its prediction using machine learning techniques. We use a raw database provided by Basque Health Service, Spain, to conduct this study. This database contains 150 156 patients diagnosed with T2DM, from whom 321 laboratory and medical assessment variables recorded over four years are available. Predictions of adverse events on T2DM patients using both classical machine learning and deep learning techniques were performed and evaluated using accuracy, precision, recall and F1-score as metrics. The best performance for the prediction of acute myocardial infarction is obtained by linear discriminant analysis (LDA) and support vector machines (SVM) both balanced and weight models with an accuracy of 97%; hospital admission for avoidable causes best performance is obtained by LDA balanced and SVMs balanced both with an accuracy of 92%. For the prediction of the incidence of at least one adverse event, the model with the best performance is the recurrent neural network trained with a balanced dataset with an accuracy of 94.6%. The ability to perform and compare these experiments was possible through the use of a workstation with multi-GPUs. This setup allows for scalability to larger datasets. Such models are also cloud ready and can be deployed on similar architectures hosted on AWS for even larger datasets.
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    Variational quantum classifier forbBinary classification: real vs synthetic dataset
    (Institute of Electrical and Electronics Engineers Inc., 2022) Maheshwari, Danyal; Sierra-Sosa, Daniel; García-Zapirain, Begoña
    Nowadays, quantum-enhanced methods have been widely studied to solve machine learning related problems. This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. We utilized three datasets: a synthetic dataset with randomly generated values between 0 and 1, the publicly available University of California Intelligence Machine learning (UCI) sonar dataset consisting of mining data, and a proprietary diabetes dataset related to diabetes with acute diseases and diabetes without acute disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively.