Computer-assisted Diagnosis in Gastroenterology

References

• Ledley R S, Lusted L B. Reasoning foundations of medical diagnosis. Science 1959; 130: 9–21
   PubMed Web of Science ®Google Scholar
• Warner H R, Toronto A F, Veasy L G. Experience with Bayes' theorem for computer diagnosis of congenital heart disease. Ann NY Acad Sci 1964; 115: 558–67
   PubMed Web of Science ®Google Scholar
• Heathfield H A, Wyatt J. Philosophies for the design and development of clinical decision-support systems (with discussion). Meth Inform Med 1993; 32: 1–17
   PubMed Web of Science ®Google Scholar
• van Bemmel J H, McCray A T. Sharing knowledge and information: yearbook of medical informatics '93. Schattauer, Stuttgart 1993
   Google Scholar
• Miller P L. Expert Critiquing Systems. Springer, New York 1986
   Google Scholar
• Hertz J A, Hertz B G, Christensen E, Gluud C. Use of neural networks in survival analysis. Abstract. ISCB-13, Copenhagen 1992; 67
   Google Scholar
• Lucas P JF, Janssens A R. Second evaluation of HEPAR, an expert system for the diagnosis of disorders of the liver and biliary tract. Liver 1991; 11: 340–6
   PubMedGoogle Scholar
• Hilden J, Habbema J DF. Evaluation of decision aids—more to think about. Med Inform 1990; 15: 275–84
   PubMed Web of Science ®Google Scholar
• McAdam W AF, Davenport P, Brock B M, et al. Twelve years' experience of computer-aided diagnosis in a district general hospital. Ann R Coll Surg Engl 1990; 72: 140–46
   PubMed Web of Science ®Google Scholar
• Wyatt J, Spiegelhalter D. Medical expert systems: what to test and how?. Systems Engineering in Medicine, J Talmon, J Fox. Springer, Heidelberg etc 1990
   Google Scholar
• Habbema J DF, Hilden J, Bjerregaard B. The measurement of performance in probabilistic diagnosis. I. The problem, descriptive tools, and measures based on classification matrices. Meth Inform Med 1978; 17: 217–26
   PubMed Web of Science ®Google Scholar
• Hilden J, Habbema J DF, Bjerregaard B. The measurement of performance in probabilistic diagnosis. II. Trustworthiness of the exact values of the diagnostic probabilities. Meth Inform Med 1978; 17: 227–37
   PubMed Web of Science ®Google Scholar
• Hilden J, Habbema J DF, Bjerregaard B. The measurement of performance in probabilistic diagnosis. III. Methods based on continuous functions of the diagnostic probabilities. Meth Inform Med 1978; 17: 238–46
   PubMed Web of Science ®Google Scholar
• Habbema J DF, Hilden J. The measurement of performance in probabilistic diagnosis. IV. Utility considerations in therapeutics and prognostics. Meth Inform Med 1981; 20: 80–96
   PubMed Web of Science ®Google Scholar
• Habbema J DF, Hilden J, Bjerregaard B. The measurement of performance in probabilistic diagnosis. V. General recommendations. Meth Inform Med 1981; 20: 97–100
   PubMed Web of Science ®Google Scholar
• Hilden J, Bjerregaard B. Computer-aided diagnosis and the atypical case. Decision making and medical care: Can information science help?, F T de Dombal, F Grémy. North-Holland Publishing Company, Amsterdam 1976; 365: 78
   Google Scholar
• Hilden J, Habbema J DF. Prognosis in medicine: an analysis of its meaning and its roles. Theor Medicine 1987; 8: 349–65
   PubMedGoogle Scholar
• Hilden J. The area under the ROC curve and its competitors. Med Decision Making 1991; 11: 95–101
   PubMed Web of Science ®Google Scholar
• Hoffmann J, Rasmussen O Ø. Aids in the diagnosis of acute appendicitis. Br J Surg 1989; 76: 774–9
   PubMed Web of Science ®Google Scholar
• Horrocks J C, McCann A P, Staniland J R, et al. Computer-aided diagnosis: description of an adaptable system, and operational experience with 2,034 cases. Br Med J 1972; 2: 5–9
   PubMedGoogle Scholar
• de Dombal F T, Leaper D J, Staniland J R, et al. Computer-aided diagnosis of acute abdominal pain. Br Med J 1972; 2: 9–13
   PubMed Web of Science ®Google Scholar
• de Dombal F T, Leaper D J, Horrocks J C, et al. Human and computer-aided diagnosis of abdominal pain: further report with emphasis on performance of clinicans. Br Med J 1974; 1: 376–80
   PubMed Web of Science ®Google Scholar
• Bjerregaard B, Brynitz S, Holst-Christensen J, . Computer-aided diagnosis of the acute abdomen: a system from Leeds used on Copenhagen patients. Decision making and medical care. Can information science help?, F T de Dombal, F Grémy, et al. North-Holland Publishing Company, Amsterdam 1976; 165–75
   Google Scholar
• Bjerregaard B. Computerhjælp i den diagnostiske beslutning-sproces. PhD Thesis. University of Copenhagen. 1978
   Google Scholar
• de Dombal F T. The OMGE acute abdominal pain survey—progress report, 1986. Scand J Gastroenterol 1988; 23(Suppl 144)35–42
   PubMed Web of Science ®Google Scholar
• Bjerregaard B, Brynitz S, Holst-Christensen J, et al. The reliability of medical history and physical examination in patients with acute abdominal pain. Meth Inform Med 1983; 22: 15–8
   PubMed Web of Science ®Google Scholar
• de Dombal F T, Matharu S S, Staniland J R, et al. Presentation of cancer to hospital as 'acute abdominal pain'. Br J Surg 1980; 67: 413–6
   PubMed Web of Science ®Google Scholar
• Jess P, Bjerregaard B, Brynitz S, et al. Prognosis of acute nonspecific abdominal pain. A prospective study. Am J Surg 1982; 144: 338–40
   PubMed Web of Science ®Google Scholar
• Adams I D, Chan M, Clifford P C, et al. Computer-aided diagnosis of acute abdominal pain: a multicentre study. Br Med J 1986; 293: 800–04
   PubMed Web of Science ®Google Scholar
• Scarlett P Y, Cooke W M, Clarke D, et al. Computer-aided diagnosis of acute abdominal pain at Middlesbrough General Hospital. Ann R Coll Surg Engl 1986; 68: 179–81
   PubMed Web of Science ®Google Scholar
• Bégon F, Dhumeaux D. The application of computer techniques to the laboratory diagnosis. Minnesota Med 1971; 54: 101–5
   PubMedGoogle Scholar
• Croft D J. Is computerized diagnosis possible?. Comput Biomed Res 1972; 5: 351–67
   PubMedGoogle Scholar
• Fraser P M, Franklin D A. Mathematical models for the diagnosis of liver disease. Q J Med 1974; 43: 73–88
   PubMedGoogle Scholar
• Martin W B, Apostolakos P C, Roazen H. Clinical versus actuarial prediction in the differential diagnosis of jaundice. Am J Med Sci 1960; 240: 571–8
   PubMed Web of Science ®Google Scholar
• Schenker S, Balint J, Schiff L. Differential diagnosis of jaundice: report of a prospective study of 61 proved cases. Am J Dig Dis 1962; 7: 449–63
   PubMedGoogle Scholar
• Burbank F. A computer diagnostic system for the diagnosis of prolonged undifferentiating liver disease. Am J Med 1969; 46: 401–15
   PubMed Web of Science ®Google Scholar
• Winkel P, Ramsoe K, Lyngbye J, Tygstrup N. Diagnostic value of routine liver tests. Clin Chem 1975; 21: 71–5
   PubMed Web of Science ®Google Scholar
• Solberg H E, Skrede S, Blomhoff J P. Diagnosis of liver diseases by laboratory results and discriminant analysis. Scand J Clin Lab Invest 1975; 35: 713–21
   PubMed Web of Science ®Google Scholar
• Goldberg D M, Ellis G. Mathematical and computer-assisted procedures in the diagnosis of liver and biliary tract disorders. Adv Clin Chem 1978; 20: 49–127
   PubMedGoogle Scholar
• Darmoni S J, Poynard T. Computer-aided decision support in hepatology. Scand J Gastroenterol 1992; 27: 889–96
   PubMed Web of Science ®Google Scholar
• Knill-Jones R P, Stern R B, Girmes D H, et al. Use of sequential Bayesian model in diagnosis of jaundice by computer. Br Med J 1973; i: 530–3
   Google Scholar
• Stern R B, Knill-Jones R P, Williams R. Use of computer program for diagnosing jaundice in district hospitals and specialized liver unit. Br Med J 1975; ii: 657–62
   Google Scholar
• Alperovitch A, Le Minor M, Lellouch J. Three examples of computer-aided medical decision. Decision making and medical care: Can information science help?, F T de Dombal, F Grémy. North Holland Publishing Company, Amsterdam 1976; 143: 9
   Google Scholar
• Lindberg G. Studies on diagnostic decision making in jaundice. PhD thesis. University of Stockholm 1982
   Google Scholar
• Euricterus Project Management Group. EURICTERUS: 2 year progress report. Theor Surg 1991; 6: 59–62
   Google Scholar
• Matzen P, Malchow-Møller A, Hilden J, et al. Differential diagnosis of jaundice: a pocket diagnostic chart. Liver 1984; 4: 360–71
   PubMedGoogle Scholar
• Malchow-Møller A, Thomsen C, Matzen P, et al. Computer diagnosis in jaundice. Bayes' rule founded on 1002 consecutive cases. J Hepatol 1986; 3: 154–63
   PubMed Web of Science ®Google Scholar
• Malchow-Møller A, Thomsen C, Hilden J, et al. A decision tree for early differentiation between obstructive and non-obstructive jaundice. Scand J Gastroenterol 1988; 23: 391–401
   PubMed Web of Science ®Google Scholar
• de Dombal F T (1983) Towards a more objective evaluation of computer-aided decision support systems. Proceedings of the fourth world conference on medical informatics, Amsterdam, August 22–27, 1983, J H van Bemmel, M J Ball, O Wigertz. North-Holland Publishing Company, 436–9
   Google Scholar
• Lindberg G, Thomsen C, Malchow-Møller A, Matzen P, Hilden J. Differential diagnosis of jaundice: applicability of the Copenhagen pocket chart proved in Stockholm patients. Liver 1987; 7: 43–9
   PubMedGoogle Scholar
• Boom R, Gonzales C, Fridman L, et al. Looking for indicants in the differential diagnosis of jaundice. Med Decision Making 1986; 6: 36–41
   PubMed Web of Science ®Google Scholar
• Segaar R W, Wilson J HP, Habbema J DF, Malchow-Møller A, Hilden J, van der Maas P J. Transferring a diagnostic decision aid for jaundice. Neth J Med 1988; 33: 5–15
   PubMed Web of Science ®Google Scholar
• Stern R B, Knill-Jones R P, Williams R. Clinician versus computer in the choice of 11 differential diagnoses of jaundice based on formalised data. Meth Inform Med 1974; 13: 79–82
   PubMed Web of Science ®Google Scholar
• Haubek A, Pedersen J H, Burcharth F, Gammelgaard J, Hancke S, Willumsen L. Dynamic sonography in the evaluation of jaundice. Am J Radiol 1981; 136: 1071–4
   Google Scholar
• Matzen P, Malchow-Møller A, Brun B, et al. Ultrasonography, computed tomography and cholescintigraphy in suspected obstructive jaundice—a comparative study. Gastroenterology 1983; 84: 1492–7
   PubMed Web of Science ®Google Scholar
• Matzen P, Haubek A, Holst-Christensen J, Lejerstofte J, Juhl E. Accuracy of direct cholangiography by endoscopic or transhepatic route in jaundice. A prospective study. Gastroenterology 1981; 81: 237–41
   PubMed Web of Science ®Google Scholar
• Pasanen P A, Partanen K, Pikkarainen P, Alhava E, Pirinen A, Janatuinen E. Diagnostic accuracy of ultrasound, computer tomography, and endoscopic retrograde cholangiopancreatography in the detection of obstructive jaundice. Scand J Gastroenterol 1991; 26: 1157–64
   PubMed Web of Science ®Google Scholar
• Malchow-Møller A, Grønvall S, Hilden J, et al. Ultrasound examination in jaundiced patients. Is computer-assisted preclassification helpful?. J Hepatol 1991; 12: 321–6
   PubMed Web of Science ®Google Scholar
• Dowie J, Elstein A. Professional judgment: a reader in clinical decision making. Cambridge University Press, Cambridge 1988
   Google Scholar
• Hilden J. Intuition and other soft modes of thought in surgery. Theor Surg 1991; 6: 89–94
   Google Scholar
• Edwards N H. The accuracy of a Bayesian computer program for diagnosis and teaching in acute abdominal pain of childhood. Comput Meth Prog Biomed 1986; 23: 155–60
   PubMed Web of Science ®Google Scholar
• de Dombal F T, Dallos V, McAdam W AF. Can computer aided teaching packages improve clinical care in patients with acute abdominal pain?. Br Med J 1991; 302: 1495–7
   PubMedGoogle Scholar
• Card W I, Lucas R W. Computer interrogation in medical practice. Int J Man-Machine Studies 1981; 14: 49–57
   Google Scholar