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?].
【作为不知情的量子力学的新医生: 适应Dirac推理系统的最佳实践是个性化医学，基因组学和蛋白质组学吗？】
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DOI：10.1021/pr070098h文章类型：杂志文章
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在个性化医疗的医疗决策支持中，自动推理的最佳实践是什么？已知的系统已经存在于使用bra-kets 和bras 的量子力学 (QM) 中的Dirac推理系统中，其中A和B是表示例如临床和生物医学规则的状态，事件或测量值。Dirac的系统在理论上应该是所有推论的普遍最佳实践，尽管QM臭名昭著，因为有时会得出奇怪的结论，这些结论似乎不适用于日常世界人类经验和医疗实践的宏观世界。这里有人认为，如果为QM分配一个新的乘法函数 @，则这种明显的困难就消失了，该函数适当地保留了条件性，从而使QM适用于包括谓词演算的定量形式在内的经典推理。具有相同后果的另一种解释是，如果狄拉克QM中的每个i = radical-1都被h代替，h是一个与1和i不同的实体，并且可以说是1的隐藏根，使得h2 = 1。除了这个例外，本文主要是对Dirac系统应用的回顾，通过在复杂域中应用线性代数来帮助操纵有关复杂数据中关联和本体的信息。任何组合的bra-ket 都可以显示为仅由类似QM的bra和ket权重c() 的总和，乘以Fano互信息度量I(A; B) 关于A和B之间的关联，即来自数据挖掘的关联规则。使用Riemann的不完整 (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，可以很容易地利用现实世界稀疏数据的实际观测值计数。最后，本文比较了相同的特性，状态事件或测量的可区分性，相关性，互信息和正交特性，数据挖掘和生物医学分析中的重要问题 (如QM)。
