What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket can be shown to be composed only of the sum of QM-like bra and ket weights c(), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining and biomedical analytics, as in QM.
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【The new physician as unwitting quantum mechanic: is adapting Dirac's inference system best practice for personalized medicine, genomics, and proteomics?].
【新的医师是不知不觉中的量子力学：是否正在将Dirac的推理系统最佳实践应用于个性化医学，基因组学和蛋白质组学？】
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DOI：10.1021/pr070098h文章类型：杂志文章
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：针对个性化医学的医疗决策支持中自动推理的最佳实践是什么？量子力学（QM）中使用Brakets 和文胸其中A和B是代表临床和生物医学规则的状态，事件或度量。从理论上讲，狄拉克的系统应该是所有推理的通用最佳实践，尽管QM是臭名昭著的，因为有时会导致一些奇怪的结论，这些结论似乎不适用于人类日常经验和医学实践的宏观世界。有人认为，如果为QM分配一个新的乘法函数@，则这个明显的困难就消失了，它适当地保留了条件，使QM适用于经典推论，包括谓词演算的定量形式。具有相同结果的另一种解释是，如果狄拉克质量控制中的每个i =自由基1都被h取代，h是一个不同于1和i的实体，并且可以说是1的隐藏根，使得h2 = 1。因此，首先通过复杂领域中线性代数的应用来回顾Dirac系统的应用，以帮助处理有关复杂数据中的关联和本体的信息。任何组合式护垫仅由类QM的胸罩和ket权重c（）之和乘以Fano互信息测度I（A; B）的指数函数得出。 A和B之间的关联，即数据挖掘中的关联规则。使用Riemann的Incomplete（即广义）Zeta函数将权重和Fano度量重新表示为对有限数据的期望时，可以轻松利用实际稀疏数据的实际观察计数。最后，与QM一样，本文比较了相同特征，状态事件或测量的可区分性，相关性，互信息和正交特征，以及数据挖掘和生物医学分析中的重要问题。
