In this study, a medical knowledge graph is constructed from the electronic medical record text of knee osteoarthritis patients to support intelligent medical applications such as knowledge retrieval and decision support, and to promote the sharing of medical resources. After constructing the domain ontology of knee osteoarthritis and manually labeling, we trained a machine learning model to automatically perform entity recognition and entity relation extraction, and then used a graph database to construct the knowledge graph of knee osteoarthritis. The experiment proves that the knowledge graph is comprehensive and reliable, and the knowledge graph construction method proposed in this study is effective.
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http://dx.doi.org/10.1007/s13755-020-0102-4 | DOI Listing |
Alzheimers Dement
December 2024
The University of Texas Health Science Center at Houston, Houston, TX, USA.
Background: Developing drugs for treating Alzheimer's disease (AD) has been extremely challenging and costly due to limited knowledge on underlying biological mechanisms and therapeutic targets. Repurposing drugs or their combination has shown potential in accelerating drug development due to the reduced drug toxicity while targeting multiple pathologies.
Method: To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level evidence on drug efficacy, to identify repurposable drugs and their combination candidates RESULT: Using the drug embedding from the heterogeneous graph representation model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, mechanistic efficacy in preclinical models, population-based treatment effect, and Phase 2/3 clinical trials.
Nat Med
January 2025
Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development.
View Article and Find Full Text PDFNeural Netw
January 2025
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China. Electronic address:
The production of expressive molecular representations with scarce labeled data is challenging for AI-driven drug discovery. Mainstream studies often follow a pipeline that pre-trains a specific molecular encoder and then fine-tunes it. However, the significant challenges of these methods are (1) neglecting the propagation of diverse information within molecules and (2) the absence of knowledge and chemical constraints in the pre-training strategy.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Computer and Electronic Information, Guangxi University, University Road, Nanning, 530004, Guangxi, China. Electronic address:
Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text instructions in the action space. Recent research has focused on extracting visual features and enhancing text knowledge, ignoring the potential bias in multi-modal data and the problem of spurious correlations between vision and text. Therefore, this paper studies the relationship structure between multi-modal data from the perspective of causality and weakens the potential correlation between different modalities through cross-modal causality reasoning.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, 999077, China.
Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand.
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