Li Li

ORCID: 0009-0009-6285-227X
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About
Contact & Profiles
Research Areas
  • Noise Effects and Management
  • COVID-19 diagnosis using AI
  • Emergency and Acute Care Studies
  • Urinary Tract Infections Management
  • Educational Innovations and Challenges
  • Drug-Induced Hepatotoxicity and Protection
  • Infrastructure Resilience and Vulnerability Analysis
  • Natural product bioactivities and synthesis
  • Alzheimer's disease research and treatments
  • Pharmacological Effects of Natural Compounds
  • Porphyrin and Phthalocyanine Chemistry
  • Pelvic floor disorders treatments
  • Landslides and related hazards
  • Phytochemistry and Biological Activities
  • Flood Risk Assessment and Management
  • Technology and Human Factors in Education and Health
  • Urinary Bladder and Prostate Research
  • Genomics, phytochemicals, and oxidative stress
  • Nanoplatforms for cancer theranostics
  • Neuroinflammation and Neurodegeneration Mechanisms
  • Pneumonia and Respiratory Infections
  • Photodynamic Therapy Research Studies
  • Neuroscience and Neuropharmacology Research
  • Ginseng Biological Effects and Applications

Southwest Hospital
2024

Sun Yat-sen Memorial Hospital
2024

Sun Yat-sen University
2024

Fuzhou University
2024

Jiaxing University
2024

Tianjin University of Science and Technology
2024

Tianjin University of Traditional Chinese Medicine
2024

Army Medical University
2024

Zhoushan Hospital
2022

Health and Human Development (2HD) Research Network
2021

GENERAL COMMENTARY article Front. Cell. Neurosci., 19 March 2010Sec. Cellular Neuropathology Volume 4 - 2010 | https://doi.org/10.3389/fncel.2010.00005

10.3389/fncel.2010.00005 article EN cc-by Frontiers in Cellular Neuroscience 2010-01-01

10.1109/yac63405.2024.10598630 article EN 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) 2024-06-07

<title>Abstract</title> Triage is of great importance in the procedure emergency diagnosis, which needs artificial intelligence to assist due scarcity medical resources. However, AI models for aiding triage have difficulty identifying levels that are difficult or ambiguous human clinicians distinguish. Here, address this challenge and improve performance models, we propose KUTS, a foundational classification model triage, leverages knowledge prompt-tuning encoder an uncertainty-based...

10.21203/rs.3.rs-4580303/v1 preprint EN cc-by Research Square (Research Square) 2024-07-25

10.1016/s0002-9378(21)00128-9 article EN American Journal of Obstetrics and Gynecology 2021-03-26
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