Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 8, Issue 5, 2024

An Expert System for Health Diagnosis Based on Natural Language Processing and Reasoning Engine

Download as PDF

DOI: 10.23977/acss.2024.080515 | Downloads: 36 | Views: 1082

Author(s)

Yawen Deng 1

Affiliation(s)

1 Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Nanhai, Foshan, China

Corresponding Author

Yawen Deng

ABSTRACT

The paper discusses expert systems in the field of digital health, which provide services for health diagnosis. An expert system is an artificial intelligence technology that simulates a doctor's diagnostic process and provides diagnostic and treatment recommendations. The paper describes the techniques and methods related to expert systems, mainly including natural language processing and inference engines. Natural language processing is used to process and understand the natural language input provided by the patient, extract key information, and convert it into a machine-understandable form. The inference engine uses the medical knowledge base and rules to perform logical reasoning and inference to generate diagnostic results and treatment recommendations. The paper also describes related experiments and evaluations, as well as ethical issues and challenges. The aim is to explore the application and development of AI in expert systems for health diagnosis to inform and inspire the field of digital health.

KEYWORDS

Expert System, Natural Language Processing, Inference Engine

CITE THIS PAPER

Yawen Deng, An Expert System for Health Diagnosis Based on Natural Language Processing and Reasoning Engine. Advances in Computer, Signals and Systems (2024) Vol. 8: 126-133. DOI: http://dx.doi.org/10.23977/acss.2024.080515.

REFERENCES

[1] Jabbar H K, Khan R Z. Survey on development of expert system in the areas of Medical, Education, Automobile and Agriculture[C]//2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2015: 776-780.
[2] Ismaila L E, Ismail I M, Agwu N N. Relevance of User Data Completeness in Resolving Differential Diagnosis in Medical Expert System Optimization[C]//2018 14th International Conference on Electronics Computer and Computation (ICECCO). IEEE, 2018: 1-6.
[3] Thakur G K, Thakur A, Khan N, et al. The Role of Natural Language Processing in Medical Data Analysis and Healthcare Automation[C]//2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024, 1: 1-5.
[4] Stefanelli M. The socio-organizational age of artificial intelligence in medicine[J]. Artificial intelligence in medicine, 2001, 23(1): 25-47.
[5] Samuel A L. Some studies in machine learning using the game of checkers[J]. IBM Journal of research and development, 1959, 3(3): 210-229.
[6] Quinlan J R. An empirical comparison of genetic and decision-tree classifiers[M]//Machine Learning Proceedings 1988. Morgan Kaufmann, 1988: 135-141.
[7] Huang S, Cai N, Pacheco P P, et al. Applications of support vector machine (SVM) learning in cancer genomics[J]. Cancer genomics & proteomics, 2018, 15(1): 41-51.
[8] Ho T K. The random subspace method for constructing decision forests[J]. IEEE transactions on pattern analysis and machine intelligence, 1998, 20(8): 832-844.
[9] Wu Y Z, Chen X C, Yi D. Advances and perspective of artificial intelligence in clinical area[J]. J. Army Med. Univ, 2022, 44: 89-102.
[10] Nassiri K, Akhloufi M A. Recent Advances in Large Language Models for Healthcare[J]. BioMedInformatics, 2024, 4(2): 1097-1143.
[11] Garg R, Gupta A. A Systematic Review of NLP Applications in Clinical Healthcare: Advancement and Challenges[C]//International Conference on Advances in Data-driven Computing and Intelligent Systems. Singapore: Springer Nature Singapore, 2023: 31-44.
[12] Wang Y, Tafti A, Sohn S, et al. Applications of natural language processing in clinical research and practice[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials. 2019: 22-25.
[13] Reggia J A, Nau D S, Wang P Y. A new inference method for frame-based expert systems[C]//Proceedings of the Third AAAI Conference on Artificial Intelligence. 1983: 333-337.
[14] Benmimoune L, Hajjam A, Ghodous P, et al. Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process[C]//2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2015: 1-5.
[15] Chen Y, Argentinis J D E, Weber G. IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research [J]. Clinical therapeutics, 2016, 38(4): 688-701.
[16] Dilsizian S E, Siegel E L. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment[J]. Current cardiology reports, 2014, 16: 1-8.
[17] Stoitsis J, Valavanis I, Mougiakakou S G, et al. Computer aided diagnosis based on medical image processing and artificial intelligence methods[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 569(2): 591-595.
[18] Zhang N, Chen M, Bi Z, et al. Cblue: A chinese biomedical language understanding evaluation benchmark[J]. arXiv preprint arXiv:2106.08087, 2021.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 38553
Visits: 697933

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.