Machine learning for holistic visualization of STEMI registry data.

BACKGROUND: Widespread adoption of evidence-based guidelines and treatment pathways in ST-Elevation Myocardial Infarction (STEMI) patients has considerably improved cardiac survival and decreased the risk of recurrent myocardial infarction. However, survival outcomes appear to have plateaued over the last decade. The hope underpinning the current study is to engage data visualization to develop a more holistic understanding of the patient space, supported by principles and techniques borrowed from traditionally disparate disciplines, like cartography and machine learning. METHODS AND RESULTS: The Minnesota Heart Institute Foundation (MHIF) STEMI database is a large prospective regional STEMI registry consisting of 180 variables of heterogeneous data types on more than 5000 patients spanning 15 years. Initial assessment and preprocessing of the registry database was undertaken, followed by a first proof-of-concept implementation of an analytical workflow that involved machine learning, dimensionality reduction, and data visualization. 38 pre-admission variables were analyzed in an all-encompassing representation of pre-index STEMI event data. We aim to generate a holistic visual representation - a map of the multivariate patient space - by training a high-resolution self-organizing neural network consisting of several thousand neurons. The resulting 2-D lattice arrangement of n-dimensional neuron vectors allowed patients to be represented as point locations in a 2-D display space. Patient attributes were then visually examined and contextualized in the same display space, from demographics to pre-existing conditions, event-specific procedures, and STEMI outcomes. Data visualizations implemented in this study include a small-multiple display of neural component planes, composite visualization of the multivariate patient space, and overlay visualization of non-training attributes. CONCLUSION: Our study represents the first known marriage of cartography and machine learning techniques to obtain visualizations of the multivariate space of a regional STEMI registry. Combining cartographic mapping techniques and artificial neural networks permitted the transformation of the STEMI database into novel, two-dimensional visualizations of patient characteristics and outcomes. Notably, these visualizations also drive the discovery of anomalies in the data set, informing corrections applied to detected outliers, thereby further refining the registry for integrity and accuracy. Building on these advances, future efforts will focus on supporting further understanding of risk factors and predictors of outcomes in STEMI patients. More broadly, the thorough visual exploration of display spaces generated through a conjunction of dimensionality reduction with the mature technology base of geographic information systems appears a promising direction for biomedical research.

Visualizing the topical structure of the medical sciences: a self-organizing map approach.

BACKGROUND: We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2) post-training geometric and semiotic transformations of the SOM tend to be limited, and (3) no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues. METHODOLOGY: Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains. CONCLUSIONS: Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid.

Clustering more than two million biomedical publications: comparing the accuracies of nine text-based similarity approaches.

BACKGROUND: We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents. METHODOLOGY: We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models--BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE. CONCLUSIONS: PubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts.

Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data.

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

The world of geography: visualizing a knowledge domain with cartographic means.

From an informed critique of existing methods to the development of original tools, cartographic engagement can provide a unique perspective on knowledge domain visualization. Along with a discussion of some principles underlying a cartographically informed visualization methodology, results of experiments involving several thousand conference abstracts will be sketched and their plausibility reflected on.