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Meta-Analysis
, 97 (49), e13421

Machine Learning Methods for Automatic Pain Assessment Using Facial Expression Information: Protocol for a Systematic Review and Meta-Analysis

Affiliations
Meta-Analysis

Machine Learning Methods for Automatic Pain Assessment Using Facial Expression Information: Protocol for a Systematic Review and Meta-Analysis

Dianbo Liu et al. Medicine (Baltimore).

Abstract

Introduction: Prediction of pain using machine learning algorithms is an emerging field in both computer science and clinical medicine. Several machine algorithms were developed and validated in recent years. However, the majority of studies in this topic was published on bioinformatics or computer science journals instead of medical journals. This tendency and preference led to a gap of knowledge and acknowledgment between computer scientists who invent the algorithm and medical researchers who may use the algorithms in practice. As a consequence, some of these prediction papers did not discuss the clinical utility aspects and were causally reported without following related professional guidelines (e.g., TRIPOD statement). The aim of this protocol is to systematically summarize the current evidences about performance and utility of different machine learning methods used for automatic pain assessments based on human facial expression. In addition, this study is aimed to demonstrate and fill the knowledge gap to promote interdisciplinary collaboration.

Methods and analysis: We will search all English language literature in the following electronic databases: PubMed, Web of Science and IEEE Xplore. A systematic review and meta-analysis summarizing the accuracy, interpretability, generalizability, and computational efficiency of machine learning methods will be conducted. Subgroup analyses by machine learning method types will be conducted.

Timeline: The formal meta-analysis will start on Jan 15, 2019 and expected to finish by April 15, 2019.

Ethics and dissemination: Ethical approval will be exempted or will not be required because the data collected and analyzed in this meta-analysis will not be on an individual level. The results will be disseminated in the form of an official publication in a peer-reviewed journal and/or presentation at relevant conferences.

Registration: PROSPERO CRD42018103059.

Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1
Figure 1
PRISMA 2009 flow diagram.

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