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Invernici F, Bernasconi A, Ceri S. Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation. J Med Internet Res 2024; 26:e52655. [PMID: 38814687 PMCID: PMC11176882 DOI: 10.2196/52655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 03/06/2024] [Accepted: 03/30/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Since the beginning of the COVID-19 pandemic, >1 million studies have been collected within the COVID-19 Open Research Dataset, a corpus of manuscripts created to accelerate research against the disease. Their related abstracts hold a wealth of information that remains largely unexplored and difficult to search due to its unstructured nature. Keyword-based search is the standard approach, which allows users to retrieve the documents of a corpus that contain (all or some of) the words in a target list. This type of search, however, does not provide visual support to the task and is not suited to expressing complex queries or compensating for missing specifications. OBJECTIVE This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19-related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience. METHODS We considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications' abstracts using terms selected from the Unified Medical Language System and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths. RESULTS We built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching. CONCLUSIONS Our approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available.
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Affiliation(s)
- Francesco Invernici
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Anna Bernasconi
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Stefano Ceri
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
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Liu N, Wang L. An approach for assisting diagnosis of Alzheimer's disease based on natural language processing. Front Aging Neurosci 2023; 15:1281726. [PMID: 38035270 PMCID: PMC10687444 DOI: 10.3389/fnagi.2023.1281726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Alzheimer's Disease (AD) is a common dementia which affects linguistic function, memory, cognitive and visual spatial ability of the patients. Language is proved to have the relationship with AD, so the time that AD can be diagnosed in a doctor's office is coming. Methods In this study, the Pitt datasets are used to detect AD which is balanced in gender and age. First bidirectional Encoder Representation from Transformers (Bert) pretrained model is used to acquire the word vector. Then two channels are constructed in the feature extraction layer, which is, convolutional neural networks (CNN) and long and short time memory (LSTM) model to extract local features and global features respectively. The local features and global features are concatenated to generate feature vectors containing rich semantics, which are sent to softmax classifier for classification. Results Finally, we obtain a best accuracy of 89.3% which is comparative compared to other studies. In the meanwhile, we do the comparative experiments with TextCNN and LSTM model respectively, the combined model manifests best and TextCNN takes the second place. Discussion The performance illustrates the feasibility to predict AD effectively by using acoustic and linguistic datasets.
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Affiliation(s)
- Ning Liu
- School of Science/School of Big Data Science, Zhejiang University of Science and Technology, Zhejiang, China
| | - Lingxing Wang
- Department of Neurology, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
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Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Pavlov M, Alonso C. Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach. Heliyon 2023; 9:e21491. [PMID: 37954345 PMCID: PMC10637999 DOI: 10.1016/j.heliyon.2023.e21491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/14/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.
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Affiliation(s)
| | - Louise Travé-Massuyès
- LAAS-CNRS, Université Fédérale de Toulouse, CNRS, UPS, INSA, Toulouse, France
- ANITI, Université Fédérale de Toulouse, Toulouse, France
| | - Audine Subias
- LAAS-CNRS, Université Fédérale de Toulouse, CNRS, UPS, INSA, Toulouse, France
| | | | - Corinne Alonso
- LAAS-CNRS, Université Fédérale de Toulouse, CNRS, UPS, INSA, Toulouse, France
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Liu N, Yuan Z, Chen Y, Liu C, Wang L. Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features. Front Aging Neurosci 2023; 15:1122799. [PMID: 37266402 PMCID: PMC10231228 DOI: 10.3389/fnagi.2023.1122799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 04/05/2023] [Indexed: 06/03/2023] Open
Abstract
Background Alzheimer's disease (AD) is difficult to diagnose on the basis of language because of the implicit emotion of transcripts, which is defined as a supervised fuzzy implicit emotion classification at the document level. Recent neural network-based approaches have not paid attention to the implicit sentiments entailed in AD transcripts. Method A two-level attention mechanism is proposed to detect deep semantic information toward words and sentences, which enables it to attend to more words and fewer sentences differentially when constructing document representation. Specifically, a document vector was built by progressively aggregating important words into sentence vectors and important sentences into document vectors. Results Experimental results showed that our method achieved the best accuracy of 91.6% on annotated public Pitt corpora, which validates its effectiveness in learning implicit sentiment representation for our model. Conclusion The proposed model can qualitatively select informative words and sentences using attention layers, and this method also provides good inspiration for AD diagnosis based on implicit sentiment transcripts.
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Affiliation(s)
- Ning Liu
- School of Science/School of Big Data Science, Zhejiang University of Science and Technology, Hangzhou, China
| | - Zhenming Yuan
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Yan Chen
- International Unresponsive Wakefulness Syndrome and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
| | - Chuan Liu
- School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, Fujian, China
| | - Lingxing Wang
- Department of Neurology, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
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Millington T. An investigation into the effects and effectiveness of correlation network filtration methods with financial returns. PLoS One 2022; 17:e0273830. [PMID: 36070303 PMCID: PMC9451073 DOI: 10.1371/journal.pone.0273830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 08/17/2022] [Indexed: 11/18/2022] Open
Abstract
When studying financial markets, we often look at estimating a correlation matrix from asset returns. These tend to be noisy, with many more dimensions than samples, so often the resulting correlation matrix is filtered. Popular methods to do this include the minimum spanning tree, planar maximally filtered graph and the triangulated maximally filtered graph, which involve using the correlation network as the adjacency matrix of a graph and then using tools from graph theory. These assume the data fits some form of shape. We do not necessarily have a reason to believe that the data does fit into this shape, and there have been few empirical investigations comparing how the methods perform. In this paper we look at how the filtered networks are changed from the original networks using stock returns from the US, UK, German, Indian and Chinese markets, and at how these methods affect our ability to distinguish between datasets created from different correlation matrices using a graph embedding algorithm. We find that the relationship between the full and filtered networks depends on the data and the state of the market, and decreases as we increase the size of networks, and that the filtered networks do not provide an improvement in classification accuracy compared to the full networks.
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Affiliation(s)
- Tristan Millington
- Usher Institute, University of Edinburgh, NINE Bioquarter, Edinburgh, United Kingdom
- * E-mail:
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Zhou X, Zhou M, Huang D, Cui L. A probabilistic model for co-occurrence analysis in bibliometrics. J Biomed Inform 2022; 128:104047. [DOI: 10.1016/j.jbi.2022.104047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 10/18/2022]
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Ambadi PS, Basche K, Koscik RL, Berisha V, Liss JM, Mueller KD. Spatio-Semantic Graphs From Picture Description: Applications to Detection of Cognitive Impairment. Front Neurol 2021; 12:795374. [PMID: 34956070 PMCID: PMC8696356 DOI: 10.3389/fneur.2021.795374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Clinical assessments often use complex picture description tasks to elicit natural speech patterns and magnify changes occurring in brain regions implicated in Alzheimer's disease and dementia. As The Cookie Theft picture description task is used in the largest Alzheimer's disease and dementia cohort studies available, we aimed to create algorithms that could characterize the visual narrative path a participant takes in describing what is happening in this image. We proposed spatio-semantic graphs, models based on graph theory that transform the participants' narratives into graphs that retain semantic order and encode the visuospatial information between content units in the image. The resulting graphs differ between Cognitively Impaired and Unimpaired participants in several important ways. Cognitively Impaired participants consistently scored higher on features that are heavily associated with symptoms of cognitive decline, including repetition, evidence of short-term memory lapses, and generally disorganized narrative descriptions, while Cognitively Unimpaired participants produced more efficient narrative paths. These results provide evidence that spatio-semantic graph analysis of these tasks can generate important insights into a participant's cognitive performance that cannot be generated from semantic analysis alone.
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Affiliation(s)
- Pranav S. Ambadi
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Kristin Basche
- Division of Geriatrics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Rebecca L. Koscik
- Wisconsin Alzheimer's Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Visar Berisha
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Julie M. Liss
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Kimberly D. Mueller
- Division of Geriatrics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, United States
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