Wang Jie, Wang Xinyi, Xin Tao
Reliability and validity of simple Chinese version of grit scale for elementary school students
BMC psychology
process labeling medical text plays crucial role medical research nonetheless creating accurately labeled medical texts high quality often timeconsuming task requires specialized domain knowledge traditional methods generating labeled data typically rely rigid rulebased approaches may adapt well new tasks recent machine learning ml methodologies mitigated manual labeling efforts configuring models align specific research requirements challenging labelers without technical expertise moreover automated labeling techniques transfer learning face difficulties directly incorporating expert input whereas semiautomated methods like data programming allow knowledge integration rules knowledge bases may lack continuous result refinement throughout entire labeling process study present collaborative humanml teaming workflow seamlessly integrates visual cluster analysis active learning assist domain experts labeling medical text high efficiency additionally introduce innovative neural network model called embedding network incorporates expert insights generate taskspecific embeddings medical texts integrate workflow embedding network visual analytics tool named kmtlabeler equipped coordinated multilevel views interactions two illustrative case studies along controlled user study provide substantial evidence effectiveness kmtlabeler creating efficient labeling environment medical text classification