| 1. |
- Andersson, Jennifer, et al.
(författare)
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Anomaly Detection for the Centralised Elasticsearch Service at CERN
- 2021
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Ingår i: Frontiers in Big Data. - : Frontiers Media S.A.. - 2624-909X. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- For several years CERN has been offering a centralised service for Elasticsearch, a popular distributed system for search and analytics of user provided data. The service offered by CERN IT is better described as a service of services, delivering centrally managed and maintained Elasticsearch instances to CERN users who have a justified need for it. This dynamic infrastructure currently consists of about 30 distinct and independent Elasticsearch installations, in the following referred to as Elasticsearch clusters, some of which are shared between different user communities. The service is used by several hundred users mainly for logs and service analytics. Due to its size and complexity, the installation produces a huge amount of internal monitoring data which can be difficult to process in real time with limited available person power. Early on, an idea was therefore born to process this data automatically, aiming to extract anomalies and possible issues building up in real time, allowing the experts to address them before they start to cause an issue for the users of the service. Both deep learning and traditional methods have been applied to analyse the data in order to achieve this goal. This resulted in the current deployment of an anomaly detection system based on a one layer multi dimensional LSTM neural network, coupled with applying a simple moving average to the data to validate the results. This paper will describe which methods were investigated and give an overview of the current system, including data retrieval, data pre-processing and analysis. In addition, reports on experiences gained when applying the system to actual data will be provided. Finally, weaknesses of the current system will be briefly discussed, and ideas for future system improvements will be sketched out.
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| 2. |
- Archetti, D, et al.
(författare)
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Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease
- 2021
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Ingår i: Frontiers in big data. - : Frontiers Media SA. - 2624-909X. ; 4, s. 661110-
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Tidskriftsartikel (refereegranskat)abstract
- Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.
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| 3. |
- D'Auria, Daniela, et al.
(författare)
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An intelligent telemonitoring application for coronavirus patients : reCOVeryaID
- 2023
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Ingår i: Frontiers in Big Data. - : Frontiers Media S.A.. - 2624-909X. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.
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| 4. |
- Elragal, Rawan, et al.
(författare)
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Healthcare analytics—A literature review and proposed research agenda
- 2023
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Ingår i: Frontiers in Big Data. - : Frontiers Media S.A.. - 2624-909X. ; 6
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Forskningsöversikt (refereegranskat)abstract
- This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field.
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| 5. |
- Froese, L, et al.
(författare)
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Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research
- 2021
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Ingår i: Frontiers in big data. - : Frontiers Media SA. - 2624-909X. ; 4, s. 689358-
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: As real time data processing is integrated with medical care for traumatic brain injury (TBI) patients, there is a requirement for devices to have digital output. However, there are still many devices that fail to have the required hardware to export real time data into an acceptable digital format or in a continuously updating manner. This is particularly the case for many intravenous pumps and older technological systems. Such accurate and digital real time data integration within TBI care and other fields is critical as we move towards digitizing healthcare information and integrating clinical data streams to improve bedside care. We propose to address this gap in technology by building a system that employs Optical Character Recognition through computer vision, using real time images from a pump monitor to extract the desired real time information.Methods: Using freely available software and readily available technology, we built a script that extracts real time images from a medication pump and then processes them using Optical Character Recognition to create digital text from the image. This text was then transferred to an ICM + real-time monitoring software in parallel with other retrieved physiological data.Results: The prototype that was built works effectively for our device, with source code openly available to interested end-users. However, future work is required for a more universal application of such a system.Conclusion: Advances here can improve medical information collection in the clinical environment, eliminating human error with bedside charting, and aid in data integration for biomedical research where many complex data sets can be seamlessly integrated digitally. Our design demonstrates a simple adaptation of current technology to help with this integration.
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| 6. |
- Markidis, Stefano
(författare)
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The Old and the New : Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?
- 2021
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Ingår i: Frontiers In Big Data. - : Frontiers Media SA. - 2624-909X. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged as a new essential tool to solve various challenging problems, including computing linear systems arising from PDEs, a task for which several traditional methods exist. In this work, we focus first on evaluating the potential of PINNs as linear solvers in the case of the Poisson equation, an omnipresent equation in scientific computing. We characterize PINN linear solvers in terms of accuracy and performance under different network configurations (depth, activation functions, input data set distribution). We highlight the critical role of transfer learning. Our results show that low-frequency components of the solution converge quickly as an effect of the F-principle. In contrast, an accurate solution of the high frequencies requires an exceedingly long time. To address this limitation, we propose integrating PINNs into traditional linear solvers. We show that this integration leads to the development of new solvers whose performance is on par with other high-performance solvers, such as PETSc conjugate gradient linear solvers, in terms of performance and accuracy. Overall, while the accuracy and computational performance are still a limiting factor for the direct use of PINN linear solvers, hybrid strategies combining old traditional linear solver approaches with new emerging deep-learning techniques are among the most promising methods for developing a new class of linear solvers.
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| 7. |
- Ngoc Phuong, Chau, et al.
(författare)
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Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito
- 2022
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Ingår i: Frontiers in Big Data. - : Frontiers Media SA. - 2624-909X. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: −48.75%, for CO, −45.76%, for SO2, −42.17%, for PM2.5, and −63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.
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| 8. |
- Qandeel, Mais, 1985-
(författare)
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Facial recognition technology : regulations, rights and the rule of law
- 2024
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Ingår i: Frontiers in Big Data. - : Frontiers Media S.A.. - 2624-909X. ; 7, s. 01-14
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Tidskriftsartikel (refereegranskat)abstract
- Despite their pronounced potential, unacceptable risk AI systems, such as facial recognition, have been used as tools for, inter alia, digital surveillance, and policing. This usage raises concerns in relation to the protection of basic freedoms and liberties and upholding the rule of law. This article contributes to the legal discussion by investigating how the law must intervene, control, and regulate the use of unacceptable risk AI systems that concern biometric data from a human-rights and rule of law perspective. In doing so, the article first examines the collection of biometric data and the use of facial recognition technology. Second, it describes the nature of the obligation or duty of states to regulate in relation to new technologies. The article, lastly, assesses the legal implications resulting from the failure of states to regulate new technologies and investigates possible legal remedies. The article uses some relevant EU regulations as an illustrative example.
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| 9. |
- Qandeel, Mais, 1985-
(författare)
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Unacceptable-Risk Technologies : Regulations, Rights and the Rule of Law
- 2024
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Ingår i: Frontiers in Big Data. - : Frontiers Media S.A.. - 2624-909X. ; , s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Despite their pronounced potential, unacceptable-risk AI systems, such as facial recognition, have been used as tools for, inter alia, digital surveillance and policing. This usage raises concerns in relation to the protection of basic freedoms and liberties and upholding the rule of law. This article contributes to the legal discussion by investigating how the law must intervene, control and regulate the use of unacceptable-risk technologies that concern biometric data from a human-rights and rule of law perspective. In doing so, the article first examines the collection of biometric data and the use of facial recognition technology. Second, it describes the nature of the obligation or duty of states to regulate in relation to new technologies. The article, lastly, assesses the legal implications resulting from the failure of states to regulate unacceptable-risk technologies and investigates possible legal remedies. The article uses some relevant EU regulations as an illustrative example.
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| 10. |
- Raket, Lars Lau
(författare)
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Statistical Disease Progression Modeling in Alzheimer Disease
- 2020
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Ingår i: Frontiers in Big Data. - : Frontiers Media SA. - 2624-909X. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration reflects not only the disease state, but also the effect of the cognitive decline on the patient's pre-disease cognitive capability. Patients with high pre-disease cognitive capabilities tend to score better on cognitive tests that are sensitive early in disease relative to patients with low pre-disease cognitive capabilities at a similar disease stage. Thus, a single assessment with a cognitive test is often not adequate for determining the stage of an AD patient. Repeated evaluation of patients' cognition over time may improve the ability to stage AD patients, and such longitudinal assessments in combinations with biomarker assessments can help elucidate the time dynamics of biomarkers. In turn, this can potentially lead to identification of markers that are predictive of disease stage and future cognitive decline, possibly before any cognitive deficit is measurable. Methods and Findings: This article presents a class of statistical disease progression models and applies them to longitudinal cognitive scores. These non-linear mixed-effects disease progression models explicitly model disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline that spans ~15 years from the earliest subjective cognitive deficits to severe AD dementia. Subsequent analyses demonstrated how direct modeling of latent factors that modify the observed data patterns provides a scaffold for understanding disease progression, biomarkers, and treatment effects along the continuous time progression of disease. Conclusions: The presented framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.
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