The process of labeling medical text plays a crucial role in medical research. Nonetheless, creating accurately labeled medical texts of high quality is often a time-consuming task that requires specialized domain knowledge. Traditional methods for generating labeled data typically rely on rigid rule-based approaches, which may not adapt well to new tasks. While recent machine learning (ML) methodologies have mitigated the manual labeling efforts, configuring models to align with specific research requirements can be challenging for labelers without technical expertise. Moreover, automated labeling techniques, such as transfer learning, face difficulties in in directly incorporating expert input, whereas semi-automated methods, like data programming, allow knowledge integration through rules or knowledge bases but may lack continuous result refinement throughout the entire labeling process. In this study, we present a collaborative human-ML teaming workflow that seamlessly integrates visual cluster analysis and active learning to assist domain experts in labeling medical text with high efficiency. Additionally, we introduce an innovative neural network model called the embedding network, which incorporates expert insights to generate task-specific embeddings for medical texts. We integrate the workflow and embedding network into a visual analytics tool named KMTLabeler, equipped with coordinated multi-level views and interactions. Two illustrative case studies, along with a controlled user study, provide substantial evidence of the effectiveness of KMTLabeler in creating an efficient labeling environment for medical text classification.

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.