Machine learning for holistic visualization of STEMI registry data

Abstract

BackgroundWidespread 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 ResultsThe 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. ConclusionOur 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.