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Machine learning methods for automatic pain assessment using facial expression information: Protocol for a systematic review and meta-analysis.

Dianbo Liu | Dan Cheng | Timothy T Houle | Lucy Chen | Wei Zhang | Hao Deng
Medicine | 2018

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.

Pubmed ID: 30544420

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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.A software package that does meta-analysis and provides results in tabular format and graphically.

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Electronic archive and distribution server for research articles providing open access to more than 850,000 e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics. Users can retrieve papers via the web interface. Registered authors may use the web interface to submit their articles to arXiv. Authors can also update their submissions if they choose, though previous versions remain available. Listings of newly submitted articles in areas of interest are available via the web interface, via RSS feeds, and by subscription to automatic email alerts.

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