Time-to-event prediction using survival analysis methods for Alzheimer's disease progression.

Introduction: Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period.

Lung cancer survival period prediction and understanding: Deep learning approaches.

Introduction: Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems.

Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning.

Purpose: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs.

Methods: We developed a deep learning construct to identify uveitis using reference mouse fundus images and further categorized the severity levels of disease into mild and severe EAU. We evaluated the performance of the model using the area under the receiver operating characteristic curve (AUC) and confusion matrices.

Identity Management Systems for the Internet of Things: A Survey Towards Blockchain Solutions.

The Internet of Things aims at connecting everything, ranging from individuals, organizations, and companies to things in the physical and virtual world. The digital identity has always been considered as the keystone for all online services and the foundation for building security mechanisms such as authentication and authorization. However, the current literature still lacks a comprehensive study on the digital identity management for the Internet of Things (IoT).

Evaluation of a real-time low-power cardiorespiratory sensor for the IoT.

A wide variety of sensors have been developed in the biomedical engineering community for telemedicine and personalized healthcare applications. However, they usually focus on sensor connectivity and embedded signal processing, at the expense of the sensing part. This observation lead to the development and exhaustive evaluation of a new ECGbased cardiorespiratory IoT sensor.

Analysis of the pen pressure and grip force signal during basic drawing tasks: The timing and speed changes impact drawing characteristics.

Writing is a complex fine and trained motor skill, involving complex biomechanical and cognitive processes. In this paper, we propose the study of writing kinetics using three angles: the pen-tip normal force, the total grip force signal and eventually writing quality assessment. In order to collect writing kinetics data, we designed a sensor collecting these characteristics simultaneously.