I'm using a T-Scan device to record occlusal contacts in a patient's mouth and would like to use this to aid in diagnosis. However, the variation of the occlusal force distribution over time is equivalent to Spatio-temporal data, and it is difficult to extract features from these data. At the same time, some quantitative indicators have been proposed in oral clinical research, such as occlusion time, disclusion time, and contact areas. It is doubtful that some of them are significantly different in normal populations and malocclusion populations. Because they are usually derived from the clinician's experience and some conflicting views emerge in the literature. Can I use meta-analysis to evaluate these indicators, just like a significant difference, and select some useful indicators as input for machine learning?
Wang Peng
A meta-analysis is a precise approach for combining the outcomes of several trials. Machine learning algorithms, on the other hand, can deal with data complexity and heterogeneity by integrating and combining test findings. Ten studies were conducted, four of which were eligible meta-analyses.
Machine learning is now being used to identify which patients are at high risk of disease and require additional support (sometimes with racial bias), to identify which molecules may lead to promising new drugs, to search for cancer in X-rays (sometimes with gender bias), and to classify tissue on pathology slides.
Dear Wang Peng
Yes, features for machine learning can be derived from medicine meta-analysis. You may refer to the following links for more details
1) Article A review on machine learning approaches and trends in drug discovery
2)Article A Machine Learning Aided Systematic Review and Meta-Analysis...
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