Latest AI and machine learning research in geriatrics for healthcare professionals.
To build a system for monitoring elderly people living alone, an important step needs to be done: identifying the presence/absence of the person being monitored and his location. Such task has several applications that we discuss in this paper, and remains very important. Several techniques were proposed in the literature. However, most of them suffer from issues related to privacy, coverage or co...
Fall detection in specialized homes for the elderly is challenging. Vision-based fall detection solutions have a significant advantage over sensor-based ones as they do not instrument the resident who can suffer from mental diseases. This work is part of a project intended to deploy fall detection solutions in nursing homes. The proposed solution, based on Deep Learning, is built on a Convolutiona...
Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clinical assessment instruments include the Delirium Ob...
If a pupil falls seriously ill, it is not only a shock for the pupil himself or herself, but also for his or her family and classmates. The project "V...
Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascular diseases are risk factors for dialysis patients....
Radiology reports include various types of clinical information that are used for patient care. Reports are also expected to have secondary uses (e.g....
Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this...
The development of artificial intelligence (AI) systems to support diagnostic decision-making is rapidly expanding in health care. However, important ...
This paper discusses a study that aimed to elicit promising application areas and potential business models for social robotics in healthcare. For thi...
This paper presents experiences of integrating assistive robots in Japanese nursing care through semi-structured interviews and site observations at t...
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's dise...
PURPOSE OF REVIEW: The Model for End-Stage Liver Disease (MELD) has been used to rank liver transplant candidates since 2002, and at the time bringing...
OBJECTIVES: Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We us...
OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed d...
Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population,...
Artificial intelligence (AI) has been studied in ophthalmology since availability of digital information in ophthalmic care. The significant turning p...
Functional connectivity networks, derived from resting-state fMRI data, have been found as effective biomarkers for identifying mild cognitive impairm...
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini M...
BACKGROUND: Accurate diagnosis of Alzheimer disease (AD) involving less invasive molecular procedures and at reasonable cost is an unmet medical need....
BACKGROUND: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease ...