Schmidt Christian, Kesztyüs Dorothea, Haag Martin, Wilhelm Manfred, Kesztyüs Tibor
Medical Data Integration Center, Department of Medical Informatics, University Göttingen, Göttingen, Germany.
GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany.
JMIR Med Educ. 2023 Mar 9;9:e43988. doi: 10.2196/43988.
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.
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.
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.
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.
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.
医学教学是一项复杂的任务，因为医学教师还参与临床实践和研究，且罕见病病例的获取非常有限。自动创建虚拟患者病例将大有裨益，既能节省时间，又能为学生培训提供更多样化的虚拟患者病例选择。
本研究探讨医学文献是否能提供关于罕见病的可用量化信息。该研究实施了一种计算机化方法，利用疾病症状出现的概率来模拟基本临床患者病例。
在医学文献中搜索合适的罕见病以及特定症状各自的概率所需信息。我们开发了一个统计脚本，根据文献中报道的概率，通过伯努利实验生成具有随机症状组合的基本虚拟患者病例。运行次数以及由此生成的患者病例数量是任意的。
我们以“脑脓肿”这一典型诊断为例，展示了我们生成器的功能，其相关症状包括“头痛、精神状态改变、局灶性神经功能缺损、发热、癫痫发作、恶心和呕吐、颈项强直以及视乳头水肿”，并给出了文献中的相应概率。随着伯努利实验重复次数的增加，出现的相对频率越来越接近文献中的概率。例如，在10000次重复后头痛的相对频率为0.7267，四舍五入后等于文献中报道的概率范围的平均值0.73。其他症状也是如此。
医学文献提供了关于罕见病特征的特定信息，这些信息可以转化为概率。我们计算机化方法的结果表明，基于这些概率自动创建虚拟患者病例是可行的。随着文献中提供更多信息，在进一步研究中可以对生成器进行扩展。
