A Method to Derive the Time of Onset of Infection from Serological Findings

F. Steimann; M. Hayde

doi:10.1055/s-0038-1634683

Methods of Information in Medicine, Table of Contents

Methods Inf Med 1997; 36(01): 51-58
DOI: 10.1055/s-0038-1634683

Original Article

Schattauer GmbH

A Method to Derive the Time of Onset of Infection from Serological Findings

Authors

F. Steimann

¹Institute of Medical Informatics, University of Hildesheim, Germany
M. Hayde

²University Children’s Hospital, University of Vienna, Austria

Abstract

Abstract:

Among the diagnostic problems requiring a retrospective assessment of the time an event occurred is that of screening for primary infection with Toxoplasma gondii acquired during pregnancy. We suggest a method to derive the possible times of onset of infection from a small sequence of serological samples by matching them against the knowledge about possible courses of infection. Special care is taken to properly address the relative change of consecutive samples, a nontrivial problem when reasoning about sparsely sampled time courses. To investigate the practicability of our approach we conducted a retrospective and a simulated prospective evaluation based on the samples of 394 pregnancies, randomly selected from our toxoplasmosis database; we could demonstrate an overall accuracy of 95.7%.

Keywords:

Temporal Reasoning - Serodiagnosis - Infection - Onset - Toxoplasma Gondii

Full Text

References

References
1 Aspöck H, Pollak H. Prevention of toxoplasmosis by serological screening of pregnant women in Austria. Scand J Infect Dis 1992; 84: 32-8 (Supplement).
2 Lappalainen M, Koskela P, Koskiniemi M. et al. Toxpplasmosis acquired during pregnancy: improved serodiagnosis based on avidity of IgG. J Infect Dis 1993; 167: 69-7.
3 McCabe RE, Remington JS. The diagnosis and treatment of toxoplasmosis. Eur J Clin Microbiol 1983; 2: 95-104.
4 Remington JS, Desmonts G. Toxoplasmosis. In: Remington JS, Klein JO. eds. Infectious Diseases of the Fetus and Newborn Infant. (Third ed.). Philadelphia: W. B. Saunders Company; 1990: 98-195.
5 Desmonts G, Couvreur J. Toxoplasmosis. In: Conn RB. eds. Current Diagnosis 7. Philadelphia: W. B. Saunders Company; 1985
6 Bessieres MR, Roques C, Berrebi A, Barre V, Cazaux M, Seguela JP. IgA antibody response during acquired and congenital toxoplasmosis. J Clin Pathol 1992; 45: 605-8.
7 Ades AE. Evaluating the sensitivity and predictive value of tests of recent infection: toxoplasmosis in pregnancy. Epiderniol Infect 1991; 107: 527-35.
8 Steirnann F, Hayde M, Panzenböck B, Adlassnig KP, Pollak A. Fuzzy support for serodiagnosis: the ONSET program. IEEE EMB Mag 1994; 13: 705-9.
9 Steirnann F. Diagnostic Monitoring of Clinical Time Series. (Doctoral Dissertation) Technical University of Vienna; Austria: 1995
10 Steirnann F. The interpretation of time-varying data with DlAMON-1. Artif Intell Med 1996; 8: 343-57.
11 Clancey WJ. Heuristic classification. Artif Intell 1985; 27: 289-350.
12 Steirnann F. A case against logic. In: Proceedings of Medlnfo ’95. Vancouver: 1995: 989-93.
13 Adlassnig KP, Horak W. Development and retrospective evaluation of HEPAXPERT-I: A routinely-used expert system for interpretive analysis of hepatitis A and B serologic findings. Artif Intell Med 1995; 7: 1-24.
14 Pohl B, Trendelenburg C. PRO. M. D. – A diagnostic expert system shell for clinical chemistry test result interpretation. Meth Inform Med 1988; 27: 111-7.
15 Pohl B, Beringer C, Walther S. et al. Recent methods for knowledge-based interpretation systems with the PRO. M. D. expert system shell (in German). Laboratoriumsmedizin 1994; 8: 577-82.
16 Coiera EW. Monitoring diseases with empirical and model-generated histories. Artif Intell Med 1990; 2: 135-47.
17 Ironi L, Stefanelli M, Lanzola G. Qualitative models in medical diagnosis. Artif Intell Med 1990; 2: 85-101.
18 Uckun S, Dawant BM. Qualitative modeling as a paradigm for diagnosis and prediction in critical care environments. Artif Intell Med 1992; 4: 127-44.
19 Kahn MG. Combining physiological models and symbolic methods to interpret time-varying patient data. Meth Inform Med 1991; 30: 167-78.
20 Larizza C, Moglia A, Stefanelli M. M-HTP: A system for monitoring heart transplant patients. Artif Intell Med 1992; 4: 111-26.
21 Shahar Y, Musen MA. RESUME: A temporal-abstraction system for patient monitoring. Comput Biomed Res 1993; 26: 255-73.
22 Allen R. Time series methods in the monitoring of intracranial pressure part I: problems, suggestions for a monitoring scheme and review of appropriate techniques. J Biomed Eng 1983; 5: 5-17.
23 Gordon K. The multi-state Kalman Filter in medical monitoring. Com put Meth Progr Biomed 1986; 23: 147-54.
24 Gordon K, Smith AFM. Modelling and monitoring biomedical time series. In: Spall JC. eds. Bayesian Analysis of Time Series and Dynamic Models. New York: Marcel Dekker; 1988
25 Avent RK, Charlton JD. A critical review of trend-detection methodologies for biomedical monitoring systems. Crit Rev Biomed Eng 1990; 17: 621-59.
26 Challis RE, Kitney RI. Biomedical signal processing (in four parts). Part 1: time domain methods. Med Biol Engin Comput 1990; 28: 509-24.
27 Sittig DF, Factor M. Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm. Comput Meth Progr Biomed 1990; 31: 1-10.
28 Sittig DF, Cheung KH. A parallel implementation of a multi-state Kalman filtering algorithm to detect ECG arrhythmias. Int J Clin Mon Comput 1992; 9: 13-22.