AIMC Topic: Aged

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A Multi-Criteria Decision-Making Model for Evaluating Senior Daycare Center Locations.

International journal of environmental research and public health
Many developed and developing countries are facing an imminent population aging and rapid demographics changing problem. The need of various kinds of eldercare is increasing tremendously. A senior daycare center, very similar to a daycare center for ...

Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.

Computer methods and programs in biomedicine
OBJECTIVES: Identifying acute exacerbations in chronic obstructive pulmonary disease (AECOPDs) is of utmost importance for reducing the associated mortality and financial burden. In this research, the authors aimed to develop identification models fo...

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Psychiatry research
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is th...

Ethics of socially assistive robots in aged-care settings: a socio-historical contextualisation.

Journal of medical ethics
Different embodiments of technology permeate all layers of public and private domains in society. In the public domain of aged care, attention is increasingly focused on the use of socially assistive robots (SARs) supporting caregivers and older adul...

Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (HCFs) based on spectral domain optical coherence tomography (SD-OCT) peripapillary retinal nerve fibe...

Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

Journal of cancer research and clinical oncology
PURPOSE: Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making.

Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.

The Lancet. Digital health
BACKGROUND: Screening for Barrett's Oesophagus (BE) relies on endoscopy which is invasive and has a low yield. This study aimed to develop and externally validate a simple symptom and risk-factor questionnaire to screen for patients with BE.

Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson's Disease.

Sensors (Basel, Switzerland)
Early diagnosis of Parkinson's diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to di...

Machine learning distilled metabolite biomarkers for early stage renal injury.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: With chronic kidney disease (CKD), kidney becomes damaged overtime and fails to clean blood. Around 15% of US adults have CKD and nine in ten adults with CKD do not know they have it.