Deep neural architectures for prediction in healthcare

TLDR

State‑of‑the‑art deep neural architectures excel in image, vision, speech, and language tasks and, when applied to healthcare, can achieve high‑accuracy prediction and diagnosis while integrating medical knowledge for better effectiveness, adaptation, and transparency. The study introduces a new class of end‑to‑end deep neural systems for disease diagnosis and personalized assessment, focusing on Parkinson’s disease by building a dedicated database for training, evaluation, and validation. The authors develop end‑to‑end deep neural architectures that are trained and tested on raw input data, then deployed as complete systems that generate diagnostic outputs. Experiments demonstrate that the systems can detect and predict Parkinson’s disease from medical imaging data.

Abstract

This paper presents a novel class of systems assisting diagnosis and personalised assessment of diseases in healthcare. The targeted systems are end-to-end deep neural architectures that are designed (trained and tested) and subsequently used as whole systems, accepting raw input data and producing the desired outputs. Such architectures are state-of-the-art in image analysis and computer vision, speech recognition and language processing. Their application in healthcare for prediction and diagnosis purposes can produce high accuracy results and can be combined with medical knowledge to improve effectiveness, adaptation and transparency of decision making. The paper focuses on neurodegenerative diseases, particularly Parkinson’s, as the development model, by creating a new database and using it for training, evaluating and validating the proposed systems. Experimental results are presented which illustrate the ability of the systems to detect and predict Parkinson’s based on medical imaging information.