Machine learning methods for research highlight prediction in biomedical effects of nanomaterial application

摘要

Recently, the studies on biomedical effects of nanomaterials have already achieved many progresses tightly relating to human health. But the large amounts of research achievements in nanomaterial journals bomb too massive data to clarify the research highlight prediction. Fortunately, automatic text mining methods can complete the extracting information from a large set of documents efficiently by machining learning methods. We used both Naive Bayes and K-means clustering algorithms on manually labeled research data sets. It is 88.62% by the Naive Bayes algorithm classification result of 5-folds cross validation on sampled libraries. By applying the optimized Naive Bayes classification model, we made the research highlight trend prediction based on research achievements of biomedical effects of nanomaterials in 22 cutting edge nanomaterial journals including 350,000 original literatures in period from 2000 to 2017. The data mining clarified the polymer nanomaterial is the most researched nanomaterial but with a decreasing trend. The research interests of metallic and carbon based nanomaterial follow the polymer one, and possess increasing trend. We could predict that the research highlight trend on biomedical effects of nanomaterials is focused on polymer, metallic and carbon based material systems in the near future. (C) 2018 Published by Elsevier B.V.