Embedding, aligning and reconstructing clinical notes to explore sepsis

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.

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

“…After assessing the remaining 197 articles, most studies (189 of 197, ie, 96%) were excluded because they had not used or attempted to use unstructured clinical text in their ML models to identify, detect, or predict sepsis onset. For instance, there were sepsis-related studies that used text but for other purposes such as mortality prediction, 61–65 phenotyping, 66 visualization, 67 exploratory data analysis, 68 and manual chart review. 69–71 Additionally, 6 articles about infection detection, 60 central venous catheter adverse events, 58 postoperative sepsis adverse events, 72–74 and septic shock identification 75 were excluded because they used manually human-curated rules instead of ML methods that automatically learn from data.…”