Geriatrics

A machine learning approach to risk assessment for alcohol withdrawal syndrome.

At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to predict alcohol withdrawal outcomes at the individual subject level using information easily attainable at patients' admission. An observational machine learning analysis using nested cross-validation and out-of-sample validation was applied to alcohol-...

DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images.

This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported r...

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be u...

Prediction of chronological and biological age from laboratory data.

Aging has pronounced effects on blood laboratory biomarkers used in the clinic. Prior studies have largely investigated one biomarker or population at...

Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that...

Deep learning based mild cognitive impairment diagnosis using structure MR images.

Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth leading disease mostly found in the aged population. ...

Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests.

AIM: The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests.

Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning.

The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is often inefficient and inconsistent. We have therefore introduc...

Prediction of Postoperative Length of Hospital Stay Based on Differences in Nursing Narratives in Elderly Patients with Epithelial Ovarian Cancer.

OBJECTIVES:  The current study sought to evaluate whether nursing narratives can be used to predict postoperative length of hospital stay (LOS) follow...

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alz...

Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis.

Functional connectivity networks (FCNs) based on functional magnetic resonance imaging (fMRI) have been widely applied to analyzing and diagnosing bra...

Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic dis...

From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.

We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings assoc...

Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study.

PURPOSE: Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure t...

AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia.

OBJECTIVE: An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are...

End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism.

Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patient...

Automatic assessment of Alzheimer's disease diagnosis based on deep learning techniques.

Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possibl...

Contemporary Rates and Predictors of Open Conversion During Minimally Invasive Radical Prostatectomy for Nonmetastatic Prostate Cancer.

To test contemporary rates and predictors of open conversion at minimally invasive (laparoscopic or robotic) radical prostatectomy (MIRP). Within th...

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases...

Deep learning prediction of falls among nursing home residents with Alzheimer's disease.

AIM: This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling and multiple complicatin...