Review of multimodal machine learning approaches in healthcare

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 an overview of multimodal machine learning…

• FN
  Friedrich Naumann Stiftung
• W/
  Wellcome / EPSRC Centre for Interventional and Surgical Sciences

  Award: 203145/Z/16/Z
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/T022205/1, EP/V002856/1