Online Transfer Learning for RSV Case Detection
This work addresses the problem of dynamically adapting machine learning models to evolving healthcare data for clinicians and researchers, but it is incremental as it builds on existing transfer learning techniques.
The authors tackled the challenge of using transfer learning for classification with sequential data when class labels are scarce by introducing Multi-Source Adaptive Weighting (MSAW), an online multi-source transfer learning method, and applied it to detect Respiratory Syncytial Virus cases from electronic health records, showing performance improvements over baselines.
Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily attributed to the scarcity of class labels. To address this challenge, we introduce Multi-Source Adaptive Weighting (MSAW), an online multi-source transfer learning method. MSAW integrates a dynamic weighting mechanism into an ensemble framework, enabling automatic adjustment of weights based on the relevance and contribution of each source (representing historical knowledge) and target model (learning from newly acquired data). We demonstrate the effectiveness of MSAW by applying it to detect Respiratory Syncytial Virus cases within Emergency Department visits, utilizing multiple years of electronic health records from the University of Pittsburgh Medical Center. Our method demonstrates performance improvements over many baselines, including refining pre-trained models with online learning as well as three static weighting approaches, showing MSAW's capacity to integrate historical knowledge with progressively accumulated new data. This study indicates the potential of online transfer learning in healthcare, particularly for developing machine learning models that dynamically adapt to evolving situations where new data is incrementally accumulated.