mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python

TL;DR

mmid is a Python package that integrates multi-modal healthcare data for analysis, enabling improved disease prediction, imputation, and reproducibility in complex real-world scenarios.

Contribution

The paper introduces mmid, a comprehensive Python toolkit for multi-modal healthcare data integration, prediction, and analysis, with a focus on reproducibility and handling missing data.

Findings

Multi-modal data improves cardiovascular risk prediction over single sources.

mmid effectively imputes missing data with minimal performance loss.

Application on UK Biobank data demonstrates real-world utility.

Abstract

mmid (Multi-Modal Integration and Downstream analyses for healthcare analytics) is a Python package that offers multi-modal fusion and imputation, classification, time-to-event prediction and clustering functionalities under a single interface, filling the gap of sequential data integration and downstream analyses for healthcare applications in a structured and flexible environment. mmid wraps in a unique package several algorithms for multi-modal decomposition, prediction and clustering, which can be combined smoothly with a single command and proper configuration files, thus facilitating reproducibility and transferability of studies involving heterogeneous health data sources. A showcase on personalised cardiovascular risk prediction is used to highlight the relevance of a composite pipeline enabling proper treatment and analysis of complex multi-modal data. We thus employed mmid in an example real application scenario involving cardiac magnetic resonance imaging, electrocardiogram, and polygenic risk scores data from the UK Biobank. We proved that the three modalities captured joint and individual information that was used to (1) early identify cardiovascular disease before clinical manifestations with cardiological relevance, and (2) do it better than single data sources alone. Moreover, mmid allowed to impute partially observable data modalities without considerable performance losses in downstream disease prediction, thus proving its relevance for real-world health analytics applications (which are often characterised by the presence of missing data).