Silvia D Olabarriaga

Academisch Medisch Centrum Universiteit van Amsterdam | AMC · e-Science Research Group

Acute care demands fast response and procedures from the healthcare professionals involved in the emergency. The availability of electronic medical records (EMR) enables acute care teams to access patient data promptly, which is critical for proper treatment. The EMR contains sensitive data, so proper access control is a necessity. However, acute c...

Background Accurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and...

Cross-organisation data sharing is challenging because all the involved organisations must agree on ’how’ and ’why’ the data is processed. Due to a lack of transparency, the organisations need to trust that others comply with the agreements and regulations. We propose to exploit blockchain and smart contracts technologies to define an Attribute-Bas...

When performing a systematic review, researchers screen the articles retrieved after a broad search strategy one by one, which is time-consuming. Computerised support of this screening process has been applied with varying success. This is partly due to the dependency on large amounts of data to develop models that predict inclusion. In this paper,...

Acute care demands the collaboration of multiple healthcare professionals and various organisations. During the emergency, the availability of Electronic Medical Records (EMR) allows acute care teams to access a patient's data promptly, which facilitates the decision-making process. Cloud solutions offer an environment to store and share patients'...

The enormous pressure of the increasing case numbers during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. Especially the field of algorithmic contact tracing, an area of research that had previously received limited attention, has moved into the...

UNSTRUCTURED The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited...

Background: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We...

Consensus mechanisms in blockchain applications allow mistrusting peers to agree on the global state of the chain. Most of the existing consensus mechanisms, however, are constrained by low efficiency and high energy consumption. In this paper, we propose the Blockchain Reputation-Based Consensus (BRBC) mechanism in which a node must have the reput...

Cyberattacks against healthcare institutions threaten patient care. The risk of being targeted by a damaging attack is increased when medical devices are used which rely on unmaintained legacy software that cannot be replaced and may have publicly known vulnerabilities. This review aims to provide insight into solutions presented in the literature...

In emergency care, fast and efficient treatment is vital. The availability of Electronic Medical Records (EMR) allows healthcare professionals to access a patient’s data promptly, which facilitates the decision-making process and saves time by not repeating medical procedures. Unfortunately, the complete EMR of a patient is often not available duri...

Purpose To investigate the viability of convolutional neural networks (CNNs) for the detection and volumetric segmentation of subarachnoid hemorrhage (SAH) in non-contrast computed tomography (NCCT). Materials and methods We developed and trained a CNN for the SAH segmentation by splitting a set of 302 baseline NCCTs into a training (268) and a va...

Background and purpose Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. Objective To assess the value of convolutional neural networks (CNNs) in the automat...

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role in treatment selection and prognosis. Radiological biomarkers require expert annotation and are sub...

Availability of medical records during an emergency situation is of paramount importance since it allows healthcare professionals to access patient's data on time and properly plan the next steps that need to be taken. Cloud storage has the potential to provide a solution to the problem of data unavailability during an emergency situation. However,...

Science gateways, virtual laboratories and virtual research environments are all terms used to refer to community-developed digital environments that are designed to meet a set of needs for a research community. Specifically, they refer to integrated access to research community resources including software, data, collaboration tools, workflows, in...

In this study, we attempt to assess the value of the term Big Data when used by researchers in their publications. For this purpose, we systematically collected a corpus of biomedical publications that use and do not use the term Big Data. These documents were used as input to a machine learning classifier to determine how well they can be separate...

Introduction: About 5% of all strokes are subarachnoid hemorrhages (SAHs). Accurate segmentation and detection of SAH in CT scans is important because the SAH volume is linked to delayed cerebral ischemia and poor patient outcome. SAH segmentation is a difficult task with high interobserver variability. Previous studies reported a limited average D...

Systematic reviews are a cornerstone of today's evidence‐informed decision making. With the rapid expansion of questions to be addressed and scientific information produced, there is a growing workload on reviewers, making the current practice unsustainable without the aid of automation tools. While many automation tools have been developed and are...

Background and purpose Delayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We hypothesize that Machine Learning (ML) algorithms for the prediction of DCI using a combination of cl...

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interac...

In 2011 the term "Big Data" was introduced by Gartner [5], and since then its use in literature has ever increased, also in the (bio)medical research field [1]. Although the term Big Data is widely used, studies show that its meaning is much debated and many different definitions exist [10]. This variety of definitions may lead to different underst...

To support scientists of different disciplines, different fields of Computer Science have developed tools and infras-tructures with the aim of giving them access to vast computational resources in the easiest possible way. Such extremely complex structures have evolved naturally in the last decades both in depth and breath and, in addition to scien...

Science gateways often rely on workflow engines to execute applications on distributed infrastructures. We investigate six software architectures commonly used to integrate workflow engines into science gateways. In tight integration, the workflow engine shares software components with the science gateway. In service invocation, the engine is isola...

Nowadays, big data is a key component in (bio)medical research. However, the meaning of the term is subject to a wide array of opinions, without a formal definition. This hampers communication and leads to missed opportunities. For example, in the (bio)medical field we have observed many different interpretations, some of which have a negative conn...

The lessons learned during six years of experience in design, development, and operation of four Science Gateway (SG) generations motivated us to develop yet another generation of platforms coined “Rosemary”. At the core of Rosemary the three fundamental SG functions, namely related to data, computing, and collaboration management, are integrated t...

Autism spectrum disorder (ASD) is typified as a brain connectivity disorder in which white matter abnormalities are already present early on in life. However, it is unknown if and to which extent these abnormalities are hard-wired in (older) adults with ASD and how this interacts with age-related white matter changes as observed in typical aging. T...

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the...

The lessons learned during six years of experience in design, development, and operation of four Science Gateway (SG) generations motivated us to develop yet another generation of platforms coined “Rosemary”. At the core of Rosemary the three fundamental SG functions, namely related to data, computing, and collaboration management, are integrated t...

Automated probabilistic reconstruction of white matter pathways facilitates tractography in large studies. TRACULA (TRActs Constrained by UnderLying Anatomy) follows a Markov-chain Monte Carlo (MCMC) approach that is compute-intensive. TRACULA is available on our Neuroscience Gateway (NSG), a user-friendly environment for fully automated data proce...

Acute stroke is the leading cause of disabilities and the fourth cause of death worldwide. The treatment of stroke patients often requires fast collaboration between medical experts and fast analysis and sharing of large amounts of medical data, especially image data. In this situation, cloud technologies provide a potentially cost-effective way to...

The increasing size of medical imaging data, in particular time series such as CT perfusion (CTP), requires new and fast approaches to deliver timely results for acute care. Cloud architectures based on graphics processing units (GPUs) can provide the processing capacity required for delivering fast results. However, the size of CTP datasets makes...

Science Gateways (SGs) have emerged as systems that facilitate access to cyberinfrastructures. There is a growing interest in the exploitation and development of SGs. However, it remains challenging to understand and design SG with the required properties because of the complex nature of SGs. Additionally, it is difficult to decide upon frameworks...

Discoveries in modern science can take years and involve the contribution of large amounts of data, many people and various tools. Although good scientific practice dictates that findings should be reproducible, in practice there are very few automated tools that actually support traceability of the scientific method employed, in particular when va...

The sustainability of science gateways has been a topic of active discussion because they have been created and supported in the context of temporary research and infrastructure projects. As successful projects come to an end, it is necessary to find (new) models to secure continuous exploitation of products generated by these projects. Taking this...

In computer-aided drug design, software tools are used to narrow down possible drug candidates, thereby reducing the amount of expensive in vitro research, by a process called virtual screening. This process includes large computations that require advanced computing infrastructure; however, using rapidly evolving high-performance computing platfor...

Various WS-PGRADE/gUSE science gateways have been extensively used in educational contexts, supporting courses offered by different European universities and organizations. This chapter presents some examples of how WS-PGRADE/gUSE generic and customized gateways have been used in such courses. These examples include practical cases from a variety o...

Workflow management has been widely adopted by scientific communities as a valuable tool to carry out complex experiments. It allows for the possibility to perform computations for data analysis and simulations, whereas hiding details of the complex infrastructures underneath. There are many workflow management systems that offer a large variety of...

Computational neuroscientists face challenges to manage ever-increasing large volume of data and to process them with applications that require great computational power. The Brain Imaging Centre of the Academic Medical Centre of the University of Amsterdam is a community of neuroscientists who are involved in various computational neuroscience res...

In computer-aided drug design, software tools are used to narrow down possible drug candidates, therefore reducing the amount of expensive in vitro research by a process called virtual screening. However, searching for drug candidates among a huge number of alternatives requires extensive computation. In this paper, we describe a science gateway fo...

Background This study set out to determine whether structural changes are present outside the thalamus after thalamotomy in patients with essential tremor (ET), specifically in the cerebellorubrothalamic tracts. We hypothesized that diffusion tensor imaging (DTI) would detect these changes. Methods We collected DTI scans and analysed differences i...

Science gateways provide UIs and high-level services to access and manage applications and data collections on distributed resources. They facilitate users to perform data analysis on distributed computing infrastructures without getting involved into the technical details. The e-BioInfra Gateway is a science gateway for biomedical data analysis on...

Researchers want to analyse Health Care data which may requires large pools of compute and data resources. To have them they need access to Distributed Computing Infrastructures (DCI) To use them it requires expertise which researchers may not have. Workflows can hide infrastructures. There are many workflow systems but they are not interoperable....

Medical imaging processing algorithms can be computationally very demanding. Currently, computers with multiple computing devices, such as multi-core CPUs, GPUs, and FPGAs, have emerged as powerful processing environments. These so called heterogeneous platforms have potential to significantly accelerate medical imaging applications. In this study,...

Scientific workflow management is heavily used in our organization. After six years, a large number of workflows are available and regularly used to run biomedical data analysis experiments on distributed infrastructures, mostly on grids. In this paper we present our first efforts to better understand and characterise these workflows. We start with...

Grid computing and workflow management systems emerged as solutions to the challenges arising from the processing and storage of shear volumes of data generated by modern simulations and data acquisition devices. Workflow management systems usually document the process of the workflow execution either as structured provenance information or as log...

Neuroimaging is a field that benefits from distributed computing infrastructures (DCIs) to perform data processing and analysis, which is often achieved using Grid workflow systems. Collaborative research in neuroimaging requires ways to facilitate exchange between different groups, in particular to enable sharing, re-use and interoperability of ap...

Current workflow abstractions in general lack: (a) an adequate approach to handle distributed data and (b) proper separation between logical tasks and data-flow from their mapping onto physical locations. As the complexity and dynamism of data and processing distribution have increased, optimized mapping of logical tasks to physical resources have...

Background: Major depressive disorder (MDD) is characterized by abnormalities in both brain structure and function within a frontolimbic network. However, little is known about the relation between structural and functional abnormalities in MDD. Here, we used a multimodal neuroimaging approach to investigate the relation between structural connect...

Science gateways provide user interfaces and highlevel services to access and manage applications and data collections on distributed resources. They facilitate users to perform data analysis on distributed computing infrastructures (DCIs) without getting involved into the technical details. The e-BioInfra Gateway is a science gateway for biomedica...

Background: Information Security is important for e-Science research groups and other small organisations that design and operate science gateways and virtual research environments, especially when such environments are being used for (bio)medical research. We propose a novel method to do risk assessments: MISRAM, the Model-based Information Securi...

Patients with mild cognitive impairment (MCI) do not always convert to dementia. In such cases, abnormal neuropsychological test results may not validly reflect cognitive symptoms due to brain disease, and the usual brain-behavior relationships may be absent. This study examined symptom validity in a memory clinic sample and its effect on the assoc...

Biomedical researchers can leverage Grid computing technology to address their increasing demands for data- and compute-intensive data analysis. However, usage of existing Grid infrastructures remains difficult for them. The e-infrastructure for biomedical science (e-BioInfra) is a platform with services that shield middleware complexities, in part...

Scientific experiments in a variety of domains are producing increasing amounts of data that need to be processed efficiently. Distributed Computing Infrastructures are increasingly important in fulfilling these large-scale computational requirements.

Analyzing Diffusion Tensor Image data of the human brain of large study groups is complex and demands new, sophisticated and computationally intensive pipelines that can efficiently be executed. We present our progress over the past five years in the development and porting of the DTI analysis pipeline to a grid infrastructure. Starting with simple...

Prolonged stress can have long-lasting effects on cognition. Animal models suggest that deficits in executive functioning could result from alterations within the mesofrontal circuit. We investigated this hypothesis in soldiers before and after deployment to Afghanistan and a control group using functional and diffusion tensor imaging. Combat stres...

European laws on privacy and data security are not explicit about the storage and processing of genetic data. Especially whole-genome data is identifying and contains a lot of personal information. Is processing of such data allowed in computing grids? To find out, we looked at legal precedents in related fields, current literature, and interviews...

Scientific research has become very data and compute intensive because of the progress in data acquisition and measurement devices, which is particularly true in Life Sciences. To cope with this deluge of data, scientists use distributed computing and storage infrastructures. The use of such infrastructures introduces by itself new challenges to th...

Neuroimaging is a field that benefits from distributed computing infrastructures (DCIs) to perform data- and compute-intensive processing and analysis. Using grid workflow systems not only automates the processing pipelines, but also enables domain researchers to implement their expertise on how to best process neuroimaging data. To share this expe...

Computational neuroscience is a new field of research in which neurodegenerative diseases are studied with the aid of new imaging techniques and computation facilities. Researchers with different expertise collaborate in these studies. A study requires scalable computational and storage capacity and information management facilities to succeed. Man...