Abstract Efficient knowledge extraction from Big Data is quite a challenging topic. Recognizing relevant concepts from unannotated data while considering both context and domain knowledge is critical to implementing successful knowledge extraction. In this research, we provide a novel platform we call Active Learning Integrated with Knowledge Extraction (ALIKE) that overcomes the challenges of context awareness and concept extraction, which have impeded knowledge extraction in Big Data. We propose a method to extract related concepts from unorganized data with different contexts using multiple agents, synergy, reinforcement learning, and active learning. We test ALIKE on the datasets of the COVID-19 Open Research Dataset Challenge. The experiment result suggests that the ALIKE platform can more efficiently distinguish inherent concepts from different papers than a non-agent-based method (without active learning) and that our proposed approach has a better chance to address the challenges of knowledge extraction with heterogeneous datasets. Moreover, the techniques used in ALIKE are transferable across any domain with multidisciplinary activity.
To solve the difficulty of medical data sharing in traditional medical information systems, we proposed an electronic medical record secure-sharing scheme based on the Blockchain technique. The encrypted text of the patient's electronic medical record is stored in the cloud server while the metadata of the medical record and access strategy is stored in the blockchain system. We employed smart contracts in the blockchain system to achieve user rights management. We used the decentralized, tamper-proof, and traceable features of the blockchain to realize the safe sharing of electronic medical records. The experimental results of security analysis show that the method can defend against potential network attacks while satisfying patient privacy protection and confidentiality. This study verifies the feasibility and great operating efficiency of the blockchain-based electronic medical record security sharing scheme.Clinical relevance— Our proposed blockchain-based electronic medical record-sharing scheme has great potential for the safe access of third-party users to patient data.
The chest X-ray is a simple and economical medical aid for auxiliary diagnosis and therefore has become a routine item for residents' physical examinations. Based on 40167 images of chest radiographs and corresponding reports, we explore the abnormality classification problem of chest X-rays by taking advantage of deep learning techniques. First of all, since the radiology reports are generally templatized by the aberrant physical regions, we propose an annotation method according to the abnormal part in the images. Second, building on a small number of reports that are manually annotated by professional radiologists, we employ the long short-term memory (LSTM) model to automatically annotate the remaining unlabeled data. The result shows that the precision value reaches 0.88 in accurately annotating images, the recall value reaches 0.85, and the F1-score reaches 0.86. Finally, we classify the abnormality in the chest X-rays by training convolutional neural networks, and the results show that the average AUC value reaches 0.835.
The healthcare profile of an individual is scattered across multiple data sources which can be difficult to access in a timely fashion. Furthermore, while the need to secure an individual’s personal health record is of paramount importance to prevent compromises such as cyber-attacks, it is important to be able to be able to seamlessly and quickly share information across healthcare providers to further enable precision and personalized health care. We present IntelliMedChain, a blockchain-powered knowledge-driven data sharing framework that gives patients complete control of their medical data and which can extract rich information hidden in the medical records using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for a secure data sharing amongst stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We conduct a pilot study of the IntelliMedChain network using Ethereum blockchain technology to share knowledge across stakeholders. We show how it mitigates the issues around scalability by efficiently managing large-scale data and interoperability through seamless adoption of data regulations, as prescribed by various regulatory bodies for efficient governance.
Healthcare interoperability unfolds the way for personalized healthcare services at a reduced cost. Furthermore, a decentralized system holds the promise to prevent compromises such as cyber-attacks due to data breaches. Hence, there is a need for a framework that seamlessly integrates and shares data across the system stakeholders. We propose SENSIBLE, a blockchain-powered, knowledge-driven data-sharing framework that gives patients complete control of their medical history and can extract rich information hidden in it using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for secure data sharing amongst stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We present a Proof-of-Concept of the SENSIBLE network with Ethereum to share dynamic knowledge across stakeholders. Dynamic knowledge generation on the blockchain provides a two-fold advantage of cooperation and communication amongst the stakeholders in the healthcare ecosystem. This leads to operational ease through sharing relevant portions of complex information while also ensuring the isolation of sensitive medical data.
Abstract The potential role of whole genome duplication (WGD) in evolution is controversial. Whereas some view WGD mainly as detrimental and an evolutionary ‘dead end’, there is growing evidence that the long-term establishment of polyploidy might be linked to environmental change, stressful conditions, or periods of extinction. However, despite much research, the mechanistic underpinnings of why and how polyploids might be able to outcompete non-polyploids at times of environmental upheaval remain indefinable. Here, we improved our recently developed bio-inspired framework, combining an artificial genome with an agent-based system, to form a population of so-called Digital Organisms (DOs), to examine the impact of WGD on evolution under different environmental scenarios mimicking extinction events of varying strength and frequency. We found that, under stable environments, DOs with non-duplicated genomes formed the majority, if not all, of the population, whereas the numbers of DOs with duplicated genomes increased under dramatically challenging environments. After tracking the evolutionary trajectories of individual artificial genomes in terms of sequence and encoded gene regulatory networks (GRNs), we propose that increased complexity, modularity, and redundancy of duplicated GRNs might provide DOs with increased adaptive potential under extinction events, while ensuring mutational robustness of the whole GRN. Our results confirm the usefulness of our computational simulation in studying the role of WGD in evolution and adaptation, helping to overcome the traditional limitations of evolution experiments with model organisms, and provide some additional insights into how genome duplication might help organisms to compete for novel niches and survive ecological turmoil.
Knowledge service is the integration of knowledge and services. Introducing the concept of "people-oriented" in pervasive computing to knowledge service, this paper constructs a kind of pervasive knowledge service model by means of multidisciplinary research achievements such as intelligence computing, system science, ecology, system science, knowledge science and service science, especially generalized computing and generalized learning proposed by us. At the same time, the key technologies of pervasive knowledge service are discussed.
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