Novel sparse matrix algorithm expands the feasible size of a self-organizing map of the knowledge indexed by a database of peer-reviewed medical literature
This work addresses the challenge of efficiently mapping large-scale medical literature databases for researchers and practitioners, though it appears incremental as it focuses on algorithmic improvements for an existing method.
The researchers tackled the problem of mapping the entire Medline database by developing a novel sparse matrix algorithm, which enabled the application of a self-organizing map to the full dataset, overcoming previous memory and processing limitations.
Past efforts to map the Medline database have been limited to small subsets of the available data because of the exponentially increasing memory and processing demands of existing algorithms. We designed a novel algorithm for sparse matrix multiplication that allowed us to apply a self-organizing map to the entire Medline dataset, allowing for a more complete map of existing medical knowledge. The algorithm also increases the feasibility of refining the self-organizing map to account for changes in the dataset over time.