Search Results - "Wang, Yanshan"

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  1. 1

    A comparison of word embeddings for the biomedical natural language processing by Wang, Yanshan, Liu, Sijia, Afzal, Naveed, Rastegar-Mojarad, Majid, Wang, Liwei, Shen, Feichen, Kingsbury, Paul, Liu, Hongfang

    Published in Journal of biomedical informatics (01-11-2018)
    “…[Display omitted] •Word embeddings trained from clinical notes, literature, Wikipedia, and news are compared.•Word embeddings trained from clinical notes and…”
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    Journal Article
  2. 2

    Clinical information extraction applications: A literature review by Wang, Yanshan, Wang, Liwei, Rastegar-Mojarad, Majid, Moon, Sungrim, Shen, Feichen, Afzal, Naveed, Liu, Sijia, Zeng, Yuqun, Mehrabi, Saeed, Sohn, Sunghwan, Liu, Hongfang

    Published in Journal of biomedical informatics (01-01-2018)
    “…[Display omitted] •A literature review for clinical information extraction applications.•1917 publications were identified for title and abstract…”
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  3. 3

    MedSTS: a resource for clinical semantic textual similarity by Wang, Yanshan, Afzal, Naveed, Fu, Sunyang, Wang, Liwei, Shen, Feichen, Rastegar-Mojarad, Majid, Liu, Hongfang

    Published in Language resources and evaluation (01-03-2020)
    “…The adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely…”
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  4. 4

    A clinical text classification paradigm using weak supervision and deep representation by Wang, Yanshan, Sohn, Sunghwan, Liu, Sijia, Shen, Feichen, Wang, Liwei, Atkinson, Elizabeth J, Amin, Shreyasee, Liu, Hongfang

    “…Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Machine…”
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  5. 5

    Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation by Cusick, Marika, Adekkanattu, Prakash, Campion, Thomas R., Sholle, Evan T., Myers, Annie, Banerjee, Samprit, Alexopoulos, George, Wang, Yanshan, Pathak, Jyotishman

    Published in Journal of psychiatric research (01-04-2021)
    “…Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this…”
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  6. 6

    Emerging opportunities of using large language models for translation between drug molecules and indications by Oniani, David, Hilsman, Jordan, Zang, Chengxi, Wang, Junmei, Cai, Lianjin, Zawala, Jan, Wang, Yanshan

    Published in Scientific reports (10-05-2024)
    “…A drug molecule is a substance that changes an organism’s mental or physical state. Every approved drug has an indication, which refers to the therapeutic use…”
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  7. 7

    New insight into the synergy of nitrogen-related sites on biochar surface for sulfamethoxazole adsorption from water by Wang, Chuanbin, Kong, Lingchao, Wang, Yanshan, Cui, Xiaoqiang, Li, Ning, Yan, Beibei, Chen, Guanyi

    Published in Chinese chemical letters (01-09-2023)
    “…In-depth exploration of the relationship among different adsorption sites is conducive to design of efficient adsorbents for target pollutants removal from…”
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  8. 8

    Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Total Hip Arthroplasty by Wyles, Cody C., Tibbo, Meagan E., Fu, Sunyang, Wang, Yanshan, Sohn, Sunghwan, Kremers, Walter K., Berry, Daniel J., Lewallen, David G., Maradit-Kremers, Hilal

    “…BACKGROUND:Manual chart review is labor-intensive and requires specialized knowledge possessed by highly trained medical professionals. Natural language…”
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  9. 9

    RFtest: A Robust and Flexible Community-Level Test for Microbiome Data Powerfully Detects Phylogenetically Clustered Signals by Zhang, Lujun, Wang, Yanshan, Chen, Jingwen, Chen, Jun

    Published in Frontiers in genetics (24-01-2022)
    “…Random forest is considered as one of the most successful machine learning algorithms, which has been widely used to construct microbiome-based predictive…”
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  10. 10

    Atrial overexpression of microRNA-27b attenuates angiotensin II-induced atrial fibrosis and fibrillation by targeting ALK5 by Wang, Yanshan, Cai, Heng, Li, Hongmei, Gao, Zhisheng, Song, Kunqing

    “…Atrial fibrosis influences atrial fibrillation (AF) development by transforming growth factor beta 1 (TGF-β1)/Smad pathway. Although microRNAs are implicated…”
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  11. 11

    EGFR tyrosine kinase inhibitors activate autophagy as a cytoprotective response in human lung cancer cells by Han, Weidong, Pan, Hongming, Chen, Yan, Sun, Jie, Wang, Yanshan, Li, Jing, Ge, Weiting, Feng, Lifeng, Lin, Xiaoying, Wang, Xiaojia, Wang, Xian, Jin, Hongchuan

    Published in PloS one (02-06-2011)
    “…Epidermal growth factor receptor tyrosine kinase inhibitors gefitinib and erlotinib have been widely used in patients with non-small-cell lung cancer…”
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  12. 12

    Natural language processing for populating lung cancer clinical research data by Wang, Liwei, Luo, Lei, Wang, Yanshan, Wampfler, Jason, Yang, Ping, Liu, Hongfang

    “…Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate…”
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  13. 13
  14. 14

    Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision by Shen, Zitao, Schutte, Dalton, Yi, Yoonkwon, Bompelli, Anusha, Yu, Fang, Wang, Yanshan, Zhang, Rui

    “…Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle status changes and interventions…”
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  15. 15

    Selected articles from the BioCreative/OHNLP challenge 2018 by Liu, Sijia, Wang, Yanshan, Liu, Hongfang

    “…Introduction The wide adoption of electronic health records (EHRs) has led to an improvement in healthcare quality by electronically documenting a patient’s…”
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  16. 16

    2.5 kW Narrow Linewidth Linearly Polarized All-Fiber MOPA With Cascaded Phase-Modulation to Suppress SBS Induced Self-Pulsing by Wang, Yanshan, Feng, Yujun, Ma, Yi, Chang, Zhe, Peng, Wanjing, Sun, Yinhong, Gao, Qingsong, Zhu, Rihong, Tang, Chun

    Published in IEEE photonics journal (01-06-2020)
    “…As a major limitation for power scaling of high power narrow linewidth fiber master oscillator power amplifiers (MOPAs), Stimulated Brillouin Scattering (SBS)…”
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  17. 17

    Origin, Migration, and Characterization of Gas in the Xinglongtai Area, Liaohe Subbasin (Northeast China): Insight from Geochemical Evidence and Basin Modeling by Yang, Sibo, Li, Meijun, Wang, Yanshan, Xiao, Hong, Huang, Shuangquan, Kang, Wujiang, Wang, Fangzheng

    Published in Energies (Basel) (01-09-2023)
    “…Buried hill zones in the rift basins have a significant impact on the enrichment of natural gas resources, and this is of great significance for exploration…”
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  18. 18

    Cellular Senescence in Intervertebral Disc Aging and Degeneration: Molecular Mechanisms and Potential Therapeutic Opportunities by Silwal, Prashanta, Nguyen-Thai, Allison M, Mohammad, Haneef Ahamed, Wang, Yanshan, Robbins, Paul D, Lee, Joon Y, Vo, Nam V

    Published in Biomolecules (Basel, Switzerland) (01-04-2023)
    “…Closely associated with aging and age-related disorders, cellular senescence (CS) is the inability of cells to proliferate due to accumulated unrepaired…”
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  19. 19

    Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study by Oniani, David, Chandrasekar, Premkumar, Sivarajkumar, Sonish, Wang, Yanshan

    Published in JMIR AI (04-05-2023)
    “…Natural language processing (NLP) has become an emerging technology in health care that leverages a large amount of free-text data in electronic health records…”
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  20. 20

    An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study by Sivarajkumar, Sonish, Kelley, Mark, Samolyk-Mazzanti, Alyssa, Visweswaran, Shyam, Wang, Yanshan

    Published in JMIR medical informatics (08-04-2024)
    “…Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or…”
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