Heckerling P S
Dept. of Medicine, Univ. of Illinois, Chicago.
Methods Inf Med. 1990 Jan;29(1):61-6.
Diagnostic tests provide information about the presence or absence of disease. However, even after application of diagnostic tests, significant uncertainty about the state of the patient often remains. This uncertainty can be quantified through the use of information theory. The "information" contained in diagnostic tests published in the medical literature of the years 1982 through 1986 was evaluated using Shannon information functions. Information content, averaged over all prior probabilities of disease, ranged from 0.002 bits to 0.720 bits of information; the tests therefore provided from 0.3% to 100% of the information needed for diagnostic certainty. Median average information was 0.395 bits, corresponding to only 55% of the information required for diagnostic certainty. Reclassifying test results into multiple mutually exclusive outcome categories allowed extraction of a median of 14% and a maximum of 109% more average information than that obtained using a dichotomous positive/negative classification. We conclude that the "information" provided by many of the tests published in the medical literature is insufficient to overcome diagnostic uncertainty. Information theory can quantify the uncertainty associated with diagnostic testing and suggest strategies for reducing this uncertainty.
诊断测试可提供有关疾病存在与否的信息。然而，即便进行了诊断测试，患者病情往往仍存在很大的不确定性。这种不确定性可通过信息论来量化。利用香农信息函数对1982年至1986年医学文献中发表的诊断测试所包含的“信息”进行了评估。疾病所有先验概率的平均信息含量在0.002比特至0.720比特之间；因此，这些测试提供了诊断确定性所需信息的0.3%至100%。平均信息的中位数为0.395比特，仅相当于诊断确定性所需信息的55%。将测试结果重新分类为多个相互排斥的结果类别，与使用二分法阳性/阴性分类相比，平均可多提取14%的信息，最多可多提取109%的信息。我们得出结论，医学文献中发表的许多测试所提供的“信息”不足以克服诊断不确定性。信息论可以量化与诊断测试相关的不确定性，并提出降低这种不确定性的策略。
