SAIN: Search-And-INfer, a Mathematical and Computational Framework for Personalised Multimodal Data Modelling with Applications in Healthcare

Abstract

Personalised modelling has become dominant in personalised medicine and precision health. It creates a computational model for an individual based on large data repositories of existing personalised data, aiming to achieve the best possible personal diagnosis or prognosis and derive an informative explanation for it. Current methods are still working on a single data modality or treating all modalities with the same method. The proposed method, SAIN (Search-And-INfer), offers better results and an informative explanation for classification and prediction tasks on a new multimodal object (sample) using a database of similar multimodal objects. The method is based on different distance measures suitable for each data modality and introduces a new formula to aggregate all modalities into a single vector distance measure to find the closest objects to a new one, and then use them for a probabilistic inference. This paper describes SAIN and applies it to two types of multimodal data, cardiovascular diagnosis and EEG time series, modelled by integrating modalities, such as numbers, categories, images, and time series, and using a software implementation of SAIN.