A rule-based expert system for laboratory diagnosis of hemoglobin disorders

被引:0
|
作者
Nguyen, AND
Hartwell, EA
Milam, JD
机构
关键词
D O I
暂无
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
Objective.-To illustrate the utility of a rule-based expert system in diagnosing hemoglobin disorders. Design.-A rule-based expert system was developed for diagnosing hemoglobin disorders. This expert system runs on IBM-compatible personal computers and uses a backward-chaining search strategy to draw conclusions. Laboratory data (ie, results of hemoglobin electrophoresis, quantitative measurements of hemoglobin F and hemoglobin A2 levels, and result of a sickle cell screen) are processed by the system using defined rules to obtain a set of differential diagnoses. Additional data, such as hematologic parameters, ethnicity of the patient, and the presence or absence of certain clinical signs and symptoms, aid in making a final diagnosis. The rules in the current version of this expert system include diagnostic criteria for 71 hemoglobin disorders. Setting.-Regional academic medical center. Patients.-We tested the system by using 58 survey sample cases offered by the College of American Pathologists during the period of January 1989 through December 1994. Main Outcome Measure.-The established diagnosis for a given case must be included in the list of differential diagnoses suggested by the expert system. Results.-The expert system included the actual diagnosis as one of the top four differential diagnoses in 90% of the cases, whereas all the laboratories participating in the survey included it in 84% (mean) of the cases. Conclusion.-We propose that this user-friendly expert system is a potential tool for computer-assisted diagnosis of hemoglobin disorders.
引用
收藏
页码:817 / 827
页数:11
相关论文
共 50 条
  • [21] Type-2 Fuzzy Rule-based Expert System for Ankylosing spondylitis Diagnosis
    Maftouni, Maede
    Zarandi, M. H. Fazel
    Turksen, I. B.
    Roshani, Faezeh
    2015 Annual Meeting of the North American Fuzzy Information Processing Society DigiPen NAFIPS 2015, 2015,
  • [22] A Rule-Based Expert System for Automated Document Editing
    Varma, Sandeep
    Shivam, Shivam
    Roy, Soumya Deep
    Ray, Biswarup
    PROCEEDINGS OF THE 20TH INTERNATIONAL CONFERENCE ON COMPUTING AND INFORMATION TECHNOLOGY, IC2IT 2024, 2024, 973 : 85 - 94
  • [23] Rule-Based Expert System Dedicated for Technological Applications
    Chlebus, Edward
    Krot, Kamil
    Kuliberda, Michal
    HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART II, 2011, 6679 : 373 - 380
  • [24] A RULE-BASED EXPERT SYSTEM FOR MINING METHOD SELECTION
    BANDOPADHYAY, S
    VENKATASUBRAMANIAN, P
    CIM BULLETIN, 1987, 80 (899): : 82 - 82
  • [25] A RULE-BASED EXPERT SYSTEM FOR MINING METHOD SELECTION
    BANDOPADHYAY, S
    VENKATASUBRAMANIAN, P
    CIM BULLETIN, 1988, 81 (919): : 84 - 89
  • [26] A Fuzzy Rule-Based Expert System for Diagnosing Asthma
    Zarandi, M. H. Fazel
    Zolnoori, M.
    Moin, M.
    Heidarnejad, H.
    SCIENTIA IRANICA TRANSACTION E-INDUSTRIAL ENGINEERING, 2010, 17 (02): : 129 - 142
  • [27] Rule-based expert system for Urdu Nastaleeq justification
    Asad, M
    Butt, AS
    Chaudhry, S
    Hussain, S
    INMIC 2004: 8th International Multitopic Conference, Proceedings, 2004, : 591 - 596
  • [28] Multilayered rule-based expert system for diagnosing uveitis
    Mutawa, A. M.
    Alzuwawi, Mariam A.
    ARTIFICIAL INTELLIGENCE IN MEDICINE, 2019, 99
  • [29] Design and implementation of Intelligent Agent and Diagnosis Domain Tool for Rule-based Expert System
    Kadhim, Mohammed Abbas
    Alam, M. Afshar
    Kaur, Harleen
    2013 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE AND RESEARCH ADVANCEMENT (ICMIRA 2013), 2013, : 619 - 622
  • [30] An intelligent fuzzy inference rule-based expert recommendation system for predictive diabetes diagnosis
    Nagaraj, Palanigurupackiam
    Deepalakshmi, Perumalsamy
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2022, 32 (04) : 1373 - 1396