Machine learning in pain research.
Machine learning in pain research.
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DOI：
10.1097/j.pain.0000000000001118
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发表时间：
2018-04
期刊：
影响因子：
7.4
通讯作者：
Ultsch A
中科院分区：
文献类型：
--
作者：
Lötsch J;Ultsch A
疼痛和疼痛慢性化是尚未完全了解且尚未解决的医学问题，且发病率仍然很高。 14 人们普遍认为疼痛是一种复杂的现象。 2, 32, 72 现代计算科学方法51可以使用复杂的临床和实验数据来更好地理解疼痛的复杂性。在数据科学技术中，机器学习被称为一组方法（图1），可以自动检测数据中的模式，然后使用未发现的模式来预测或分类未来的数据，观察数据中的子组等结构，或从数据中提取适合推导新知识的信息。 11, 43 与（生物）统计一起，人工智能和机器学习旨在从数据中学习。尽管统计学可以被视为数学的一个分支，但人工智能和机器学习是从计算机科学发展而来的（参考文献 58；另见 https://en.wikipedia.org/wiki/Artificial_intelligence）。人工智能的最初定义源于阿兰·图灵，他提出了一个实验，其中两个玩家（可以是人类或人工智能）试图说服第三个人类玩家，他们也是人类。 68 如果第三个玩家无法分辨谁是机器，则人工智能测试通过。机器学习发展的重要步骤是计算机学习程序的首次创建，这是一种跳棋游戏 54 和第一个称为感知器的神经网络。 53 统计学使用数学方程对数据变量之间的概率关系进行建模，而机器学习则无需先前的知识即可从数据中学习。它的目标是算法的优化和性能，而不是在给定已知的基础数据分布的情况下分析观察的概率。尽管如此，机器学习和统计技术都在模式识别、知识发现和数据挖掘方面协同工作，并且共享部分相同的方法，例如回归，广泛应用于统计学，但也被认为是机器学习中的一种分类方法（图 1）。在目前的研究背景下，当提供与疼痛相关的数据时，机器学习方法能够学习复杂特征到已知类别的映射，即从获取参数的复杂模式预测疼痛表型类别。在机器学会了对疼痛相关表型的预测后，该算法随后可以用于新数据，从中可以识别新的但未分类的受试者的类别成员资格。然而，机器学习方法也可用于复杂疼痛相关数据中的模式识别，以揭示潜在分子背景的痕迹，或用于药物发现或重新利用环境中大数据中的知识发现。使用机器学习进行疼痛研究的报告数量不断增加，反映出当代计算科学方法的使用日益增多（表 1）。这篇综述的重点是应用于一般疼痛研究的机器学习技术，这些技术允许人们分析和预测疼痛表型，并从实验和临床疼痛相关数据中获取知识。
Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence. 14 It has been accepted that pain is a complex phenomenon. 2, 32, 72 Contemporary methods of computational science51 can use complex clinical and experimental data to better understand the complexity of pain. Among data science techniques, machine learning is referred to as a set of methods (Fig. 1) that can automatically detect patterns in data and then use the uncovered patterns to predict or classify future data, to observe structures such as subgroups in the data, or to extract information from the data suitable to derive new knowledge. 11, 43 Together with (bio) statistics, artificial intelligence and machine learning aim at learning from data. Although statistics can be regarded as a branch of mathematics, artificial intelligence and machine learning have developed from computer science (Ref. 58; see also https://en. wikipedia. org/wiki/Artificial_intelligence). The initial definition of artificial intelligence originates from Alan Turing who proposed an experiment where 2 players, who can either be human or artificial, try to convince a human third player, that they are also humans. 68 The test of artificial intelligence is passed if the third player cannot tell who is the machine. Important steps in the development of machine learning were the first creation of the computer learning program, which was a checker game, 54 and the first neural network called the perceptron. 53 Statistics uses mathematical equations to model probability relationships between data variables, whereas machine learning learns from data without the necessity of previous knowledge. It aims at optimization and performance of an algorithm rather than on the analysis of the probabilities of observations, given a known underlying data distribution. Nevertheless, both machine learning and statistics techniques are working in concert for pattern recognition, knowledge discovery, and data mining and share partly the same methods such as regression, which is used widely in statistics but is also considered as a classification method in machine learning (Fig. 1). In the present research context, when provided with painrelated data, machine-learned methods are able to learn a mapping of complex features to a known class, that is, to predict a pain phenotype class from a complex pattern of acquired parameters. After the machine has learned the prediction of a pain-related phenotype, the algorithm can subsequently be used on new data from which the class membership of a novel yet unclassified subject can be identified. However, machine learning methods can also be used for pattern recognition in complex pain-related data to reveal traces of an underlying molecular background or for knowledge discovery in big data in a drug discovery or repurposing context. The increasing use of contemporary methods of computational science is reflected in the rising number of reports using machine learning for pain research (Table 1). This review is focused on machine-learned technologies applied to general pain research that allow one to analyze and predict pain phenotypes and to obtain knowledge from experimental and clinical pain-related data.
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影响因子：
3.6
作者：
Gram, M.;Erlenwein, J.;Drewes, A. M.
通讯作者：
Drewes, A. M.
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3
作者：
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3.5
作者：
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影响因子：
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作者：
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通讯作者：
Yang SF
影响因子：
2.1
作者：
Goertzel, BN;Pennachin, C;Gurbaxani, B
通讯作者：
Gurbaxani, B