Abstract Machine learning (ML) involves algorithms learning patterns in large, complex
datasets to predict and classify. Algorithms include neural networks (NN), logistic regression …

Background Deep brain stimulation (DBS) surgery is an option for patients experiencing
medically resistant neurologic symptoms. DBS complications are rare; finding significant …

Abstract Purpose Trajectories of Rehabilitation across Complex Environments (TRaCE), a
consented prospective cohort study, addresses a critical need to better understand access to …

Background Motor vehicle accidents (MVA) represent a significant burden on health systems
globally. Tens of thousands of people are injured in Australia every year and may …

Considering the increasing demand for analyzing sequential data in various fields of our
daily lives, finding hidden patterns in a continuous flow of data is one of the interesting topics …

© 2019 Esmaili et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and …

Aim: To investigate prevalence of clinical cases with regard to health-related quality of life
(HRQoL) and its association with socio-demographic and clinical factors among adult …

The effective use of technology to improve people's health depends on factors beyond the
technologies themselves, such as the diseases that affect a population, ease of access to …

Analysis of medical data and making precise decisions by machine learning is emerging as
a hot topic in healthcare. The ultimate goal of using these techniques is to transform data …

Abstract The Hidden Markov Model (HMM) is a machine learning method applied to predict
stock values that estimate the sequence of hidden variables based on the sequence of …