Improving preliminary clinical diagnosis accuracy through knowledge filtering techniques in consultation dialogues.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 30, 2024

Abstract

BACKGROUND AND OBJECTIVE: Symptom descriptions by ordinary people are often inaccurate or vague when seeking medical advice, which often leads to inaccurate preliminary clinical diagnoses. To address this issue, we propose a deep learning model named the knowledgeable diagnostic transformer (KDT) for the natural language processing (NLP)-based preliminary clinical diagnoses.

Authors

  • Ashu Abdul
    Department of Computer Science and Engineering, SRM University-AP, Neerukonda, Mangalagiri, Guntur Dist., 522503, Andhra Pradesh, India. Electronic address: ashu.a507@gmail.com.
  • Binghong Chen
    College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
  • Siginamsetty Phani
    Department of Computer Science and Engineering, SRM University-AP, Neerukonda, Mangalagiri, Guntur Dist., 522503, Andhra Pradesh, India. Electronic address: siginamsettyphani@gmail.com.
  • Jenhui Chen
    Artificial Intelligence Research Center, Chang Gung University, Kweishan District, Taoyuan City 33302, Taiwan.

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