Review of multimodal machine learning approaches in healthcare

TL;DR

This paper reviews recent multimodal machine learning methods in healthcare, highlighting how integrating diverse data sources improves clinical decision-making and discussing key techniques, datasets, and strategies used in the field.

Contribution

It provides a comprehensive overview of recent literature on multimodal machine learning in healthcare, emphasizing data fusion techniques and datasets, which advances understanding of current approaches.

Findings

Multimodal data integration enhances diagnostic accuracy.

Fusion techniques vary across different data modalities.

Several datasets support multimodal healthcare research.

Abstract

Machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of integrating multiple sources of information for improved decision making. Clinicians typically rely on a variety of data sources including patients' demographic information, laboratory data, vital signs and various imaging data modalities to make informed decisions and contextualise their findings. Recent advances in machine learning have facilitated the more efficient incorporation of multimodal data, resulting in applications that better represent the clinician's approach. Here, we provide a review of multimodal machine learning approaches in healthcare, offering a comprehensive overview of recent literature. We discuss the various data modalities used in clinical diagnosis, with a particular emphasis on imaging data. We evaluate fusion techniques, explore existing multimodal datasets and examine common training strategies.