Machine learning in pain research

Lötsch, Jörn; Ultsch, Alfred

doi:10.1097/j.pain.0000000000001118

Cited by 142 publications

(117 citation statements)

References 74 publications

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“…Data were analysed using the R software package (version 3.3.2 for Linux; http://CRAN.R-project.org/; R Development Core Team, ) on an Intel Xeon ® computer running on Ubuntu Linux 16.04.1 64‐bit. The analysis employed several methods of machine‐learning that, as described previously (Lötsch and Ultsch, a), may be referred to as a set of methods 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 (Murphy, ; Dhar, ). More detailed descriptions including definitions of key concepts have been provided elsewhere (Lötsch and Ultsch, a).…”

Section: Methodsmentioning

confidence: 99%

“…This implies that not every variant found to be functionally associated with a persisting pain phenotype was taken into account. The resulting information for each gene, thus, comprised (1) a positive report of a gene The analysis employed several methods of machinelearning that, as described previously (L€ otsch and Ultsch, 2017a), may be referred to as a set of methods 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 (Murphy, 2012;Dhar, 2013). More detailed descriptions including definitions of key concepts have been provided elsewhere (L€ otsch and Ultsch, 2017a).…”

Section: Search Strategymentioning

confidence: 99%

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A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

Kringel

Lippmann

Parnham

et al. 2018

European Journal of Pain

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BackgroundHuman genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine‐learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain.MethodsBased on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine‐learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase.ResultsClustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research.ConclusionsThe present computational functional genomics‐based approach provided a computational systems‐biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine‐learned methods provide innovative approaches to knowledge discovery from previous evidence.SignificanceWe show that knowledge discovery in genetic databases and contemporary machine‐learned techniques can identify relevant biological processes involved in Persitent pain.

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Section: Methodsmentioning

confidence: 99%

Section: Search Strategymentioning

confidence: 99%

A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

Kringel

Lippmann

Parnham

et al. 2018

European Journal of Pain

View full text Add to dashboard Cite

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“…This was implemented as computed ABC analysis 32 that aims at dividing a set of positive numerical data into three disjoint subsets called "A," "B" and "C." Set "A" should contain the "important few," that is those elements that allow obtaining a maximum of yield with a minimal effort. 33,34 Thus, the data analysis was performed in three main steps comprising (a) data preprocessing including transformation according to the observed data distributions, which was followed by outlier detection and missing value imputation, (b) the application of feature selection techniques 35 as known from machine learning 22 and (c) the statistical assessment of the selected features using classical methods.…”

Section: Data Analysis Strategymentioning

confidence: 99%

A data science approach to the selection of most informative readouts of the human intradermal capsaicin pain model to assess pregabalin effects

Lötsch

Walter

Zunftmeister

et al. 2019

Basic Clin Pharma Tox

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Persistent and, in particular, neuropathic pain is a major healthcare problem with still insufficient pharmacological treatment options. This triggered research activities aimed at finding analgesics with a novel mechanism of action. Results of these efforts will need to pass through the phases of drug development, in which experimental human pain models are established components e.g. implemented as chemical hyperalgesia induced by capsaicin. We aimed at ranking the various readouts of a human capsaicin–based pain model with respect to the most relevant information about the effects of a potential reference analgesic. In a placebo‐controlled, randomized cross‐over study, seven different pain‐related readouts were acquired in 16 healthy individuals before and after oral administration of 300 mg pregabalin. The sizes of the effect on pain induced by intradermal injection of capsaicin were quantified by calculating Cohen's d. While in four of the seven pain‐related parameters, pregabalin provided a small effect judged by values of Cohen's d exceeding 0.2, an item categorization technique implemented as computed ABC analysis identified the pain intensities in the area of secondary hyperalgesia and of allodynia as the most suitable parameters to quantify the analgesic effects of pregabalin. Results of this study provide further support for the ability of the intradermal capsaicin pain model to show analgesic effects of pregabalin. Results can serve as a basis for the designs of studies where the inclusion of this particular pain model and pregabalin is planned.

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“…Garcia‐Chimeno et al were able to distinguish with 93% accuracy between patients with sporadic migraine, patients with chronic migraine, and patients at risk for medication overuse via feature selection techniques and machine learning analyses over diffusion tensor images and questionnaire answers related to emotion and cognition . An overview of how machine learning techniques have been used in the general context of pain research was presented by Lötsch and Ultsch …”

Section: Introductionmentioning

confidence: 99%

Subgrouping Factors Influencing Migraine Intensity in Women: A Semi‐automatic Methodology Based on Machine Learning and Information Geometry

et al. 2019

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Background Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques. Objective The aim of this study was to analyze critical features that permit the differentiation of subgroups of patients with migraine according to the intensity and frequency of attacks by using machine learning algorithms. Methods Sixty‐seven women with migraine participated. Clinical features of migraine, related disability (Migraine Disability Assessment Scale), anxiety/depressive levels (Hospital Anxiety and Depression Scale), anxiety state/trait levels (State‐Trait Anxiety Inventory), and pressure pain thresholds (PPTs) over the temporalis, neck, second metacarpal, and tibialis anterior were collected. Physical examination included the flexion‐rotation test, cervical range of cervical motion, forward head position while sitting and standing, passive accessory intervertebral movements (PAIVMs) with headache reproduction, and joint positioning sense error. Subgrouping was based on machine learning algorithms by using the nearest neighbors algorithm, multisource variability assessment, and random forest model. Results For migraine intensity, group 2 (women with a regular migraine headache intensity score of 7 on an 11‐point Numeric Pain Rating Scale [where 0 = no pain and 10 = maximum pain]) were younger and had lower joint positioning sense error in cervical rotation, greater cervical mobility in rotation and flexion, lower flexion‐rotation test scores, positive PAIVMs reproducing migraine, normal PPTs over the tibialis anterior, shorter migraine history, and lower cranio‐vertebral angles while standing than the remaining migraine intensity subgroups. The most discriminative variable was the flexion‐rotation test score of the symptomatic side. For migraine frequency, no model was able to identify differences between groups (ie, patients with episodic or chronic migraine). Conclusions A subgroup of women with migraine who had common migraine intensity was identified with machine learning algorithms.

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Machine learning in pain research

Cited by 142 publications

References 74 publications

A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

A data science approach to the selection of most informative readouts of the human intradermal capsaicin pain model to assess pregabalin effects

Subgrouping Factors Influencing Migraine Intensity in Women: A Semi‐automatic Methodology Based on Machine Learning and Information Geometry

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