Embedding, aligning and reconstructing clinical notes to explore sepsis

• Main Authors: Xudong Zhu, Joseph M. Plasek, Chunlei Tang, Wasim Al-Assad, Zhikun Zhang, Yun Xiong, Liqin Wang, Sharmitha Yerneni, Carlos Ortega, Min-Jeoung Kang, Li Zhou, David W. Bates, Patricia C. Dykes
• Format: Article
• Language: English
• Published: BMC 2021-04-01
• Series: BMC Research Notes
• Subjects: Sepsis; Representation learning; Exploratory analysis; Data driven medicine
• Online Access: https://doi.org/10.1186/s13104-021-05529-4

Abstract Objective Our goal was to research and develop exploratory analysis tools for clinical notes, which now are underrepresented to limit the diversity of data insights on medically relevant applications. Results We characterize how exploratory analysis can affect representation learning on clinical narratives and present several self-developed tools to explore sepsis. Our experiments focus on patients with sepsis in the MIMIC-III Clinical Database or in our institution’s research patient data repository. We found that global embeddings assist in learning local representations of clinical notes. Second, aligning at any specific time facilitates the use of learning models by pooling more available clinical notes to form a training set. Furthermore, reconstruction of the timeline enhances downstream-processing techniques by emphasizing temporal expressions and temporal relationships in clinical documentation. We demonstrate that clustering helps plot various types of clinical notes against a scale, which conveys a sense of the range or spread of the data and is useful for understanding data correlations. Appropriate exploratory analysis tools provide keen insights into preprocessing clinical notes, thereby further enhancing downstream analysis capabilities, making data driven medicine possible. Our examples can help generate better data representation of clinical documentation for models with improved performance and interpretability.