The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach-phenotypic personalized medicine (PPM)-finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals' lives.

中文翻译：

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CURATE.AI：用人工智能优化个性化医疗。

诊断出患者后治疗的临床团队面临两个主要问题：什么治疗方法和剂量是多少？临床试验的结果为临床医生用来做出决定的官方协议提供了指导和支持的基础。然而，个体很少表现出相关临床试验报告的反应，通常是代表一个群体或亚群的群体的平均值。决策复杂性随着联合治疗而增加，其中一起给药的药物可以相互作用，这通常是这种情况。此外，个体对治疗的反应会随着他或她的状况的变化而变化，无论是通过适应症还是生理机能。在实践中，药物和剂量选择在很大程度上取决于医疗保健提供者的医疗方案和医疗团队的经验。因此，结果本质上是多变的，并且通常是次优的。大数据方法已经成为一种出色的决策支持工具，但它们的应用受到多种挑战的限制，主要挑战是具有足够大的数据集和高质量的代表性信息的可用性。另一种方法——表型个性化医疗 (PPM)——基于专门从接受治疗的个体收集的小数据，找到合适的药物组合（二次表型优化平台 [QPOP]）和合适的随时间推移的给药策略 (CURATE.AI)。基于 PPM 的方法已经证明了优于当前护理标准的结果。