Semantic similarity is a useful approach for comparing patient phenotypes, and holds the potential of an effective method for exploiting text-derived phenotypes for differential diagnosis, text and document classification, and outcome prediction. While approaches for context disambiguation are commonly used in text mining applications, forming a standard component of information extraction pipelines, their effects on semantic similarity calculations have not been widely explored. In this work, we evaluate how inclusion and disclusion of negated and uncertain mentions of concepts from text-derived phenotypes affects similarity of patients, and the use of those profiles to predict diagnosis. We report on the effectiveness of these approaches and report a very small, yet significant, improvement in performance when classifying primary diagnosis over MIMIC-III patient visits.
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http://dx.doi.org/10.3389/fdgth.2021.781227 | DOI Listing |
Cogn Neurodyn
December 2024
Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave., Waterloo, ON N2L 3G1 Canada.
Distributed vector representations are a key bridging point between connectionist and symbolic representations in cognition. It is unclear how uncertainty should be modelled in systems using such representations. In this paper we discuss how bundles of symbols in certain Vector Symbolic Architectures (VSAs) can be understood as defining an object that has a relationship to a probability distribution, and how statements in VSAs can be understood as being analogous to probabilistic statements.
View Article and Find Full Text PDFFront Plant Sci
December 2024
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: The assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.
View Article and Find Full Text PDFSignal Transduct Target Ther
December 2024
School of Basic Medical Science, Tsinghua University, 30 Shuangqing Rd., Haidian District, Beijing, 100084, China.
Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail.
View Article and Find Full Text PDFNeuroimage
December 2024
State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, 100875 Beijing, China. Electronic address:
The role of the visuospatial network in mathematical processing has been established, but the role of the semantic network in mathematical processing remains poorly understood. The current study compared different types of inductive reasoning with the functional magnetic resonance imaging (fMRI) technique to investigate the role of the semantic network in mathematical processing and whether the role is domain-general or domain-specific. 32 undergraduate students were recruited to complete tasks involving numerical, geometrical, situational, and verbal inductive reasoning, as well as arithmetical computation.
View Article and Find Full Text PDFSci Data
December 2024
Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, 1105, AZ, The Netherlands.
Faced with heterogeneity of healthcare data, we propose a novel approach for harmonizing data elements (i.e., attributes) across health data standards.
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