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Tracking the Evolution of COVID-19 via Temporal Comorbidity Analysis from Multi-Modal Data

We aim to characterize the evolution in the effectiveness of treatment for different patient groups over the course of the COVID-19 pandemic. In contrast to most existing studies, we study the evolution of patient
trajectories based on unique sets of frequent comorbid conditions discovered from the data. Further, we study the association between frequent co-morbid conditions to the length of stay (LOS) as a measure of
treatment efficacy, for poor COVID-19 related outcomes.

Rule-Based and Pattern Matching for Entity Recognition in Spark NLP

Finding patterns and matching strategies are well-known NLP procedures to extract information from text. Spark NLP library has two annotators that can...