Research on decision support applications in healthcare, such as those related to diagnosis,
prediction, treatment planning, etc., has seen strongly growing interest in recent years. This …

Machine learning (ML) methodology used in the social and health sciences needs to fit the
intended research purposes of description, prediction, or causal inference. This paper …

Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is
important for developing disease-modifying therapies. In this study, given multimodal AD …

We performed a systematic review of studies focusing on the automatic prediction of the
progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a …

Data-driven disease progression models are an emerging set of computational tools that
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …

Neurodegenerative diseases of the brain pose a major and increasing global health
challenge, with only limited progress made in developing effective therapies over the last …

Blockchain (BC) and artificial intelligence (AI) technologies have independent applications
in multiple industries, including banking, finance, healthcare, construction, transportation …

The astounding success made by artificial intelligence in healthcare and other fields proves
that it can achieve human-like performance. However, success always comes with …

The Alzheimer′ s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge is
the most comprehensive challenge to date with regard to the number of subjects, considered …

Healthcare data are inherently multimodal. Almost all data generated and acquired during a
patient's life can be hypothesized to contain information relevant to providing optimal …