Machine Learning-based Analytics Research Articles

Multimodal representation learning for medical analytics - a systematic literature review.

Objectives: Machine learning-based analytics over uni-modal medical data has shown considerable promise and is now routinely deployed in diagnostic procedures. However, patient data consists of diverse types of data. By exploiting such data, multimodal approaches promise to revolutionize our ability to provide personalized care. Attempts to combine two modalities in a single diagnostic task have utilized the evolving field of multimodal representation learning (MRL), which learns a shared latent space between related modality samples. This new space can be used to improve the performance of machine-learning-based analytics. So far, however, our understanding of how modalities have been applied in MRL-based medical applications and which modalities are best suited for specific medical tasks is still unclear, as previous reviews have not addressed the medical analytics domain and its unique challenges and opportunities. Instead, this work aims to review the landscape of MRL for medical tasks to highlight opportunities for advancing medical applications. Methods: This paper presents a framework for positioning MRL techniques and medical modalities. More than 1000 papers related to medical analytics were reviewed, positioned, and classified using the proposed framework in the most extensive review to date. The paper further provides an online tool for researchers and developers of medical analytics to dive into the rapidly changing landscape of MRL for medical applications. Results: The main finding is that work in the domain has been sparse: only a few medical informatics tasks have been the target of much MRL-based work, with the overwhelming majority of tasks being diagnostic rather than prognostic. Similarly, numerous potentially compatible information modality combinations are unexplored or under-explored for most medical tasks. Conclusions: There is much to gain from using MRL in many unexplored combinations of medical tasks and modalities. This work can guide researchers working on a specific medical application to identify under-explored modality combinations and identify novel and emerging MRL techniques that can be adapted to the task at hand.

Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.

Role of Disease Progression Models in Drug Development.

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Quantitative Confocal Microscopy for Grouping of Dose-Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine.

Fluorescent calcium (Ca2+) imaging is one of the preferred methods to record cellular activity during in vitro preclinical studies, high-content drug screening, and toxicity analysis. Visualization and analysis for dose-response data obtained using high-resolution imaging remain challenging, due to the inherent heterogeneity present in the Ca2+ spiking. To address this challenge, we propose measurement of cytosolic Ca2+ ions using spinning-disk confocal microscopy and machine learning-based analytics that is scalable. First, we implemented uniform manifold approximation and projection (UMAP) for visualizing the multivariate time-series dataset in the two-dimensional (2D) plane using Python. The dataset was obtained through live imaging experiments with norepinephrine-induced Ca2+ oscillation in HeLa cells for a large range of doses. Second, we demonstrate that the proposed framework can be used to depict the grouping of the spiking pattern for lower and higher drug doses. To the best of our knowledge, this is the first attempt at UMAP visualization of the time-series dose response and identification of the Ca2+ signature during lytic death. Such quantitative microscopy can be used as a component of a high-throughput data analysis workflow for toxicity analysis.

Interconnected Sensor Networks and Machine Learning-based Analytics in Data-driven Smart Sustainable Cities

Employing recent research results covering interconnected sensor networks and machine learning-based analytics in data-driven smart sustainable cities, and building my argument by drawing on data collected from BDO, Capgemini, ICMA, KPMG, McKinsey, Philips, SCC, SmartCitiesWorld, and World Economic Forum, I performed analyses and made estimates regarding the progress of smart cities toward sustainability. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

QoS-Aware Optical Fog-Assisted Cyber-Physical System in the 5G Ready Heterogeneous Network

In recent years, the emergence of Internet of things and cyber-physical system provide a proactive and efficacious solution to enable remote monitoring, machine learning-based analytics, and quick decision making for real-time applications. Cloud supported systems still face Quality of Service issues for implementing time-sensitive applications. Introducing 5G network services by revolutionizing existing technologies can increase communication efficiency and provide optimum trade-offs for connected IoT devices. In this paper, NFV, ONV, and SDN is used to create a computational OpticalFog node in the middleware of cloud and edge that provides an ecosystem to facilitate an ultra-fast infrastructure in the 5G network. The main aim behind the proposed system is to (1) exploit the computational potential of optical resources to create an OpticalFog node to be integrated into the 5G network infrastructure for CPS-based applications, (2) present an algorithm for optimum placement that provide service assurance to various CPS-based applications, and (3) enable CPS-based applications, which are able to run on top of the OpticalFog and 5G infrastructure. Therefore, a case study based on the deployment surveillance system in an academic institute using the proposed system is presented. iFogSim toolkit is used to implement the proposed OpticalFog-based deployment for the surveillance cameras. The results show the significant advantages of the proposed system for CPS-based applications in the 5G network.

Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson’s disease

BackgroundAlthough risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable high-risk activities to reduce future falls. ObjectivesTo explore the prediction of falling in PD patients using a machine learning-based approach. Method305 PD patients, with or without a history of falls within the past month, were recruited. Data including clinical demographics, medications, and balance confidence, scaled by the 16-item Activities-Specific Balance Confidence Scale (ABC-16), were entered into the supervised machine learning models using XGBoost to explore the prediction of fallers/recurrent fallers in two separate models. Results99 (32%) patients were fallers and 58 (19%) were recurrent fallers. The accuracy of the model to predict falls was 72% (p = 0.001). The most important factors were item 7 (sweeping the floor), item 5 (reaching on tiptoes), and item 12 (walking in a crowded mall) in the ABC-16 scale, followed by disease stage and duration. When recurrent falls were analysed, the models had higher accuracy (81%, p = 0.02). The strongest predictors of recurrent falls were item 12, 5, and 10 (walking across parking lot), followed by disease stage and current age. ConclusionOur machine learning-based study demonstrated that predictors of falling combined demographics of PD with environmental factors, including high-risk activities that require cognitive attention and changes in vertical and lateral orientations. This enables physicians to focus on modifiable factors and appropriately implement fall prevention strategies for individual patients.

Забезпечення безпеки залізничного транспорту в умовах цифровізації

У статті розглянуто стан та перспективи безпеки залізничного транспорту в умовах цифровізації. Визначено, що функціонування програми з безпеки залізничного транспорту в умовах цифровізації базується на наступних технологіях та стандартах: ERTMS, ETCS, GSM-R, GoA, ATO, ATP, ATC, ВІМ Rail, CBTC, TMS. Аналіз стану та перспективи безпеки залізничного транспорту в умовах цифровізації встановив необхідність застосування штучного інтелекту. Зазначено, що безпека залізничного транспорту зумовлена спільною взаємодією багатьох технологій, які привели до створення концепцій «Індустрія 4.0» і «Індустріальний Інтернет речей (IIoT)».

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

RES: Real-Time Video Stream Analytics Using Edge Enhanced Clouds

With increasing availability and use of Internet of Things (IoT) devices such as sensors and video cameras, large amounts of streaming data is now being produced at high velocity. Applications which require low latency response such as video surveillance, augmented reality and autonomous vehicles demand a swift and efficient analysis of this data. Existing approaches employ cloud infrastructure to store and perform machine learning-based analytics on this data. This centralized approach has limited ability to support real-time analysis of large-scale streaming data due to network bandwidth and latency constraints between data source and cloud. We propose RealEdgeStream (RES) an edge enhanced stream analytics system for large-scale, high performance data analytics. The proposed approach investigates the problem of video stream analytics by proposing (i) filtration and (ii) identification phases. The filtration phase reduces the amount of data by filtering low-value stream objects using configurable rules. The identification phase uses deep learning inference to perform analytics on the streams of interest. The phases consist of stages which are mapped onto available in-transit and cloud resources using a placement algorithm to satisfy the Quality of Service (QoS) constraints identified by a user. We demonstrate that for a 10K element data streams, with a frame rate of 15–100 per second, the job completion in the proposed system takes 49 percent less time and saves 99 percent bandwidth compared to a centralized cloud-only based approach.

COVID-19 Response and Recovery in Smart Sustainable City Governance and Management: Data-driven Internet of Things Systems and Machine Learning-based Analytics

Employing recent research results covering smart sustainable city governance and management, and building our argument by drawing on data collected from CompTIA, Deloitte, DNV GL, ICMA, KPMG, PTI, RICS, and SCC, we performed analyses and made estimates regarding data-driven Internet of Things systems and machine learning-based analytics Structural equation modeling was used to analyze the collected data © 2020, Addleton Academic Publishers All rights reserved