Understanding current states of machine learning approaches in medical informatics: a systematic literature review

摘要: Knowledge mining (KM) tends to deliver the tools and associated components extract enormous amounts of data for strategic decision-making. Numerous machine learning (ML) techniques have been applied in medical information systems. These can significantly contribute decision-making process, such as diagnosis, prediction, exploring benefits clinical care. This study aims determine insights into current state applications employed by ML field informatics (MI). We believe that this exploration would lead many unrevealed answers predictive modelling informatics. A systematic search was performed most influential scientific electronic databases one specific another database between 2016 2020 (April). Research questions are outlined after researcher has studied previous research done on subject. identified 51 related samples out 1224 searched articles satisfied our inclusion criteria. There is a significant increasing pattern application MI. In addition, popular algorithm classification problem Support Vector Machine (SVM), followed random forest (RF). contrast, "Accuracy" "Specificity" commonly used mechanisms performance indicators calculation. literature review provides new paradigm By investigation, unknown areas MI were explored.