Methods Inf Med 1991; 30(01): 23-29
DOI: 10.1055/s-0038-1634815
Original Articles
Schattauer GmbH

Development of a Scoring System to Assist in the Diagnosis of Rheumatoid Arthritis

A. S. Rigby
1   The Arthritis and Rheumatism Council Epidemiology Research Unit, University of Manchester Medical School, Manchester, U.K
› Institutsangaben
The author is grateful for the ongoing help and advice of Philip H. N. Wood.
Weitere Informationen

Publikationsverlauf

Publikationsdatum:
08. Februar 2018 (online)

During the last decade there has been a wealth of papers discussing the development of scoring systems in medicine, some of which have led to fully operational computer-aided diagnostic systems. In this paper we sketch the development of a simple scoring system for one of the more common rheumatic diseases, rheumatoid arthritis (RA) using a two-tier model, independence Bayes’ followed by logistic discrimination. The scoring system gives reasonably well calibrated probability estimates of RA which suggests ways in which the development of computerised systems in rheumatology might be approached.

 
  • REFERENCES

  • 1 Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science 1959; 130: 9-21.
  • 2 Knill-Jones RP, Stern RB, Girmes Maxwell JD, Thompson RPH, Williams R. The use of a sequential Bayesian model in the diagnosis of jaundice by computer. Br Med J 1973; 01: 530-4.
  • 3 De Dombal Leaper DJ, Staniland JR, McCann AP, Horrocks JC. Computer-aided diagnosis of acute abdomal pain. Br Med J 1972; 02: 9-13.
  • 4 Titterington DM, Murray GD, Murray LS. et al. Comparison of discrimination techniques applied to a complex data set of head injured patients. J R Statist Soc A 1981; 144: 145-75.
  • 5 Innocent PR, Teather D, Wills KM. et al. An operational system for the computer-assisted diagnosis of cerebral disease. In: van Bemmel JH, Ball MJ, Wigertz O. eds. Medinfo 83. Amsterdam: North-Holland Publ Comp; 1983: 467-70.
  • 6 Anderson JA, Boyle JA. A comparison of statistical techniques in the differential diagnosis of non-toxic goitre. Biometrics 1968; 24: 103-16.
  • 7 Boyle MA, Anderson JA. Computer diagnosis: clinical aspects. Br Med Bull 1968; 24: 224-9.
  • 8 Anderson JA, Boyle JA. Computer diagnosis: statistical aspects. Br Med Bull 1968; 24: 230-5.
  • 9 Anderson JA, Blair GS. Screening in a dental clinic for adult rheumatoid arthritis involving the temporomandibular joint using a statistical discriminant function. J Oral Rehab 1975; 02: 187-97.
  • 10 Anderson JA. Logistic discrimination with medical applications. In: Cacoullos C. ed. Discriminant Analysis and its Applications. New York: Academic Press; 1973: 1-15.
  • 11 Rigby AS, Wood PHN. A review of assignment criteria for rheumatoid arthritis. Scand J Rheumatol 1990; 19: 27-41.
  • 12 Ropes MW, Bennett GA, Cobb S, Jacox R, Jessar RA. 1958 revision of diagnostic criteria for rheumatoid arthritis. Bull Rheum Dis 1958; 4: 175-6 and Arthritis Rheum 1959; 2: 16-20.
  • 13 Spiegelhalter DJ. Statistical methodology for evaluating gastrointestinal symptoms. Clin Gastroenterol 1985; 14: 489-515.
  • 14 Good IJ. Weight of evidence, corroboration, explanatory power, information and the utility of experiments. J R Statist Soc B 1960; 22: 319-31.
  • 15 Cox DR. The Analysis of Binary Data. London: Methuen; 1970: 33.
  • 16 Knill-Jones RP. A computer assisted diagnostic system for dyspensia (GLADYS). Lecture Notes in Medical Informatics Heidelberg: Springer Verlag; 1986: 215-26.
  • 17 Crichton NJ, Fryer JG, Spicer CC. Some points on the use of “independent Bayes” to diagnose acute abdomal pain. Stat Med 1987; 06: 945-59.
  • 18 Hilden J. Statistical diagnosis based on conditional independence does not require it. Comp Biol Med 1984; 14: 429-35.
  • 19 Morton BA, Teather D, du Boulay GH. Statistical modelling and diagnostic aids. Med Decis Making 1984; 04: 339-48.
  • 20 Feinstein AR. The haze of Bayes, the aerial palaces of decision analysis, and the computerised Ouija board. Clin Pharmacol Ther 1977; 21: 482-96.
  • 21 Diagnosis: logic and psycho-logic. Lancet 1987; 01: 840-1.
  • 22 Knill-Jones RP. Diagnostic systems as an aid to clinical decision making. Br Med J 1987; 295: 1392-6.
  • 23 Spiegelhalter DJ, Knill-Jones RP. Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology. J R Statist Soc A 1984; 147: 35-77.
  • 24 Badley EM, Ball J, Wood PHN. Rheumatoid factor, so called – what does this portend for the population?. Br J Prev Soc Med 1974; 28: 71-2.
  • 25 Stern RB, Knill-Jones RP, Williams R. Clinician versus computer in the choice of 11 differential diagnoses of jaundice based on formalised data. Meth Inf Med 1974; 13: 79-82.
  • 26 De Dombal FT. Towards a more objective evaluation of computer-aided decision support systems. In: van Bemmel JH, Ball MJ, Wigertz O. eds. Medinfo 83. Amsterdam: North-Holland Publ Comp; 1983: 436-9.
  • 27 McCrea JD, McCredie MRE, McSherry DMG, Brooks PM. A controlled evaluation of diagnostic criteria in the development of a rheumatology expert system. Br J Rheumatol 1989; 28: 13-7.
  • 28 Macartney FJ. Diagnostic logic. Br Med J 1987; 295: 1325-31.
  • 29 Fox J. Formal and knowledge-based methods in decision technology. Proc Ninth Research Conf Subjective Probability, Utility and Decision Making. Groningen. 1983
  • 30 Wilkinson M. Joint Studies in Rheumatology. A Complete Course for GPs. Centre for Medical Education, University of Dundee. 1982
  • 31 Baker RJ, Nelder JR. The GLIM System (Release 3). Oxford: Numerical Algorithms Group; 1978