Fig. 3: Text processing pipeline for generating document embeddings.
From: Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data
Flowchart illustrating the five-step pipeline for constructing document embeddings. The process begins with selecting clinical notes based on a predefined character cutoff threshold, followed by text preprocessing. Next, term frequency-inverse document frequency (TF-IDF) weighting is applied to each participant’s text corpus (“patient-level corpora”). Preprocessed text is then mapped to word embeddings using BioWord2Vec, and a final document-level embedding is obtained by matrix-multiplying the TF-IDF matrix with the word embedding matrix.
