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Background Teaching medicine is a complex task because medical teachers are also involved in clinical practice and research and the availability of cases with rare diseases is very restricted. Automatic creation of virtual patient cases would be a great benefit, saving time and providing a wider choice of virtual patient cases for student training. Objective This study explored whether the medical literature provides usable quantifiable information on rare diseases. The study implemented a computerized method that simulates basic clinical patient cases utilizing probabilities of symptom occurrence for a disease. Methods Medical literature was searched for suitable rare diseases and the required information on the respective probabilities of specific symptoms. We developed a statistical script that delivers basic virtual patient cases with random symptom complexes generated by Bernoulli experiments, according to probabilities reported in the literature. The number of runs and thus the number of patient cases generated are arbitrary. Results We illustrated the function of our generator with the exemplary diagnosis “brain abscess” with the related symptoms “headache, mental status change, focal neurologic deficit, fever, seizure, nausea and vomiting, nuchal rigidity, and papilledema” and the respective probabilities from the literature. With a growing number of repetitions of the Bernoulli experiment, the relative frequencies of occurrence increasingly converged with the probabilities from the literature. For example, the relative frequency for headache after 10.000 repetitions was 0.7267 and, after rounding, equaled the mean value of the probability range of 0.73 reported in the literature. The same applied to the other symptoms. Conclusions The medical literature provides specific information on characteristics of rare diseases that can be transferred to probabilities. The results of our computerized method suggest that automated creation of virtual patient cases based on these probabilities is possible. With additional information provided in the literature, an extension of the generator can be implemented in further research.

BACKGROUND Teaching medicine is a complex task because medical teachers are also involved in clinical practice and research and the availability of cases with rare diseases is very restricted. Automatic creation of virtual patient cases would be a great benefit, saving time and providing a wider choice of virtual patient cases for student training. OBJECTIVE This study explored whether the medical literature provides usable quantifiable information on rare diseases. The study implemented a computerized method that simulates basic clinical patient cases utilizing probabilities of symptom occurrence for a disease. METHODS Medical literature was searched for suitable rare diseases and the required information on the respective probabilities of specific symptoms. We developed a statistical script that delivers basic virtual patient cases with random symptom complexes generated by Bernoulli experiments, according to probabilities reported in the literature. The number of runs and thus the number of patient cases generated are arbitrary. RESULTS We illustrated the function of our generator with the exemplary diagnosis “brain abscess” with the related symptoms “headache, mental status change, focal neurologic deficit, fever, seizure, nausea and vomiting, nuchal rigidity, and papilledema” and the respective probabilities from the literature. With a growing number of repetitions of the Bernoulli experiment, the relative frequencies of occurrence increasingly converged with the probabilities from the literature. For example, the relative frequency for headache after 10.000 repetitions was 0.7267 and, after rounding, equaled the mean value of the probability range of 0.73 reported in the literature. The same applied to the other symptoms. CONCLUSIONS The medical literature provides specific information on characteristics of rare diseases that can be transferred to probabilities. The results of our computerized method suggest that automated creation of virtual patient cases based on these probabilities is possible. With additional information provided in the literature, an extension of the generator can be implemented in further research.

Background: Teaching medicine is a complex task because medical teachers are also heavily involved in clinical practice. Automatic creation of virtual patient cases would be a great benefit to them, saving them time and providing a wider choice of virtual patient cases for student training. Our aim was to develop and test a computer program which simulates clinical case scenarios based on information about the occurrence of specific symptoms in certain diagnoses from the literature.Methods: Medical literature was searched for suitable diagnoses with information on the respective probabilities of specific symptoms. We developed a computer program that generates virtual patient cases with symptons whose occurrence is randomly generated by Bernoulli experiments, according to probabilities reported in the literature. The number of runs and thus the number of patient cases generated is arbitrary.Results: We illustrate the function of our generator with the exemplary diagnosis “brain abscess” with the related symptoms and the respective probabilities from the literature. With a growing number of repetitions of the Bernoulli experiment, the relative frequencies of occurence increasingly converge with the probabilities from the literature. E.g., the relative frequency for headache after 10.000 repetitions was 0.7267 and, after rounding, equals the mean value of the probability range of 0.73 reported in the literature. The same applies to the other symptoms. Conclusion: The results suggest that an automated creation of virtual patient cases is possible, but with regard to the limitation to symptom constellations, it is not yet suitable for professional use. Based on additional information provided in the literatur, an extension of the generator can be implemented in further research.